心理科学进展, 2018, 26(8): 1349-1364 doi: 10.3724/SP.J.1042.2018.1349

研究方法

利用时频分析研究非相位锁定脑电活动

武侠1, 钟楚鹏1, 丁玉珑1, 曲折,1

1中山大学心理系, 广州 510006

Application of time-frequency analysis in investigating non-phase locked components of EEG

WU Xia1, ZHONG Chupeng1, DING Yulong1, QU Zhe,1

1 Department of psychology, Sun Yat-sen University, Guangzhou 510006, China

通讯作者: 曲折, E-mail: quzhe@mail.sysu.edu.cn

收稿日期: 2017-10-16   网络出版日期: 2018-08-15

基金资助: 国家自然科学基金项目.  31471070
国家自然科学基金项目.  31271190

Received: 2017-10-16   Online: 2018-08-15

摘要

时频分析技术自20世纪80年代被引入到心理学脑电数据分析领域以来, 克服了传统的时域ERP方法只能分析相位锁定成分的缺陷, 可以帮助研究者挖掘到脑电信号中非相位锁定的成分。在心理学领域, 应用最多的时频分析方法是小波变换和Hilbert变换, 而能量、相位一致性和耦合是三个最常用的分析指标。研究者利用不同的分析指标来揭示不同的心智过程。不同频段的能量被认为体现了不同的认知过程, 如α能量被发现与注意选择性有关, 而γ能量则与特征整合相关。相位一致性常被用于讨论ERP产生的机制。耦合则通常说明了长距离脑区之间的信息交流以及高级脑区对低级脑区的认知控制, 在完成各种复杂认知任务的时候会表现出不同的耦合模式。

关键词: 时频分析 ; 小波变换 ; Hilbert变换 ; 能量 ; 相位一致性 ; 耦合

Abstract

Since the introduction of the time-frequency analysis technique into the field of EEG data in the 1980’s, researchers can excavate non-phase locked components in EEG signals, overcoming the previous shortcomings of traditional ERP methods. In the field of psychology, the two most commonly used time-frequency analysis methods are wavelet transform and Hilbert transform. Power, phase locking index (PLI), and coherence are three important indices of time-frequency analysis. Power in different frequency band is typically considered to reflect different mental processes. For example, α power is frequently related to selective attention, while γ energy is often associated with feature binding. Researchers use PLI to investigate the mechanism generated by an ERP component. Coherence indicates the exchange of information between long-distance brain regions and cognitive control of higher-level brain regions in the low-level brain regions, which show different patterns in various complex cognitive tasks.

Keywords: time-frequency analysis ; wavelet transform ; Hilbert transform ; power ; PLI ; coherence

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本文引用格式

武侠, 钟楚鹏, 丁玉珑, 曲折. 利用时频分析研究非相位锁定脑电活动 . 心理科学进展[J], 2018, 26(8): 1349-1364 doi:10.3724/SP.J.1042.2018.1349

WU Xia, ZHONG Chupeng, DING Yulong, QU Zhe. Application of time-frequency analysis in investigating non-phase locked components of EEG. Advances in Psychological Science[J], 2018, 26(8): 1349-1364 doi:10.3724/SP.J.1042.2018.1349

1 引言

Berger于1929年第一次记录了人类头皮处的脑电活动(Electroencephalography, EEG, Berger, 1929), 揭开了对人类EEG研究的序幕。随着时间的推进, 研究者们的研究兴趣逐渐从直接研究自发脑电活动转移到了研究事件相关电位(Event- Related Potentials, ERPs)。在1960年代以后, 人们主要通过使用时域内ERP分析方法来探讨认知活动和脑电活动之间的关系 (Bastiaansen, Mazaheri, & Jensen, 2012)。时域ERP分析方法假设每一次事件诱发的电活动类似, 而自发噪音则是随机的。时域ERP分析方法把每一次事件出现的时刻作为数据分析的标准时刻点, 将每一个试次(trial)的EEG信号按照标准时刻点对齐后叠加求平均。不同试次的噪音会在叠加平均的过程中相互抵消, 叠加平均后的脑电信号就是事件诱发的脑电活动, 代表了大脑对该事件的认知加工过程(Luck, 2005; Woodman, 2010)。

1980年代以来, 越来越多的研究者意识到时域ERP成分只是一种特定类型的事件相关脑电信号。事件发生后, 大脑对事件认知加工所对应的脑电活动中还有其他类型的事件相关脑电活动。研究者发现, 在事件发生后大脑内与事件加工相关的EEG活动有可能是以下几种类型:频率反应(frequency response, Ijspeert, 2008), 振幅反应(amplitude response, Klimesch, 2012; Tallon- Baudry & Bertrand, 1999), 相位重置(phase resetting, Gruber & Müller, 2005; Klimesch et al., 2004), 新增成分(additive response, Mäkinen, Tiitinen, & May, 2005; Mazaheri & Picton, 2005)。

图1列出了上述四类大脑对事件的反应类型。(1)频率反应(图1A)是某频率的自发EEG活动在事件出现后变化了振荡频率, 这一类大脑活动多见于单细胞放电或者人们在有节律运动的时候中枢系统某节律细胞的活动。(2)振幅反应(图1B)是事件发生后某频率EEG自发电活动能量增加(或减小), 每个试次中该频率的能量都有所增加(或减小), 不过每个试次事件相关电活动相位是随机的, 叠加平均时相互抵消掉了, ERP平均波形捕捉不到此类事件相关脑电活动。(3)相位重置(图1C)是所有试次的相位都会因事件出现而变得一致, 这类活动是相位锁定活动。求平均波形时, 此类活动会出现在平均波形中, 这是我们熟知的ERP成分。(4)新增成分(图1D), 即事件发生后每个试次都会出现与自发活动相互独立的新增成分。这些活动的相位相似, 强度相似, 求平均波形时, 也会呈现在平均结果中, 也可以产生我们所熟知的ERP成分。

图1

图1   事件发生后可能的脑电活动变化。A, 频率反应(frequency response), 事件发生后原本脑电自发活动的振荡频率发生了变化; B, 振幅反应(amplitude response), 事件发生后, 原本脑电自发活动的能量发生了变化; C, 相位重置(phase resetting), 事件发生后, 原本脑电自发活动的相位发生了扰动, 相位在试次之间变得一致; D, 新增成分(additive response), 事件发生后, 出现了一种与自发脑电活动无关的新成分, 各试次之间新成分相似。(图摘自维基百科, "Neural oscillation," 2018)


综上, 在事件发生后, 大脑对事件的认知加工过程可能表现为相位锁定的活动和非相位锁定的活动两类, 相位锁定的活动可以通过时域ERP分析方法来分析, 而相位不锁定的活动在时域ERP分析方法中被当作噪音去除掉了。即使是传统的时域ERP平均波形, 也可能有两种产生机制:相位重置(图1C)和新增活动(图1D), 但这两种机制在传统的时域ERP分析方法中无法区分。为了弥补时域ERP分析方法的这些局限性, 研究者们引入了一种新的信号分析方法, 这就是本文要介绍的时频分析方法。

2 时频分析方法原理简介

研究者从头皮处的电极记录到的脑电信号是一种时域内的信号, 是糅杂了事件诱发的短时暂变信号、自发电活动以及随机噪音等强度随时间变化的信号。为了更加清晰直观地观察脑电信号, 可以通过傅立叶变换将信号从时域变换至频域, 频域内EEG活动的各种频率成分可以清晰区分, 不仅更直观, 而且可以对不同频率成分进行独立分析, 给信号分析提供了极大的方便。但实验条件下的EEG信号是一种非稳恒信号, 事件诱发的信号有产生和衰减的时间进程。将信号完全转换到频域会失去时变信息, 导致频域结果含义不清, 难以解释。而时频分析则可以在分析频率成分的同时给出时间信息, 使得对时变信号的分析变得直观和容易理解。在结果中包含信号的频域和时域信息的分布被称为时频分布, 对信号做时频分布的分析就是时频分析。

时频分析的算法有很多种, 包括窗口傅立叶变换, 小波变换, Hilbert变换, Hilbert-Huang变换, Wigner-Ville分布等方法(Huang et al., 1998; Torrence & Compo, 1998)。在心理学脑电数据分析领域, 最常用的是小波变换和Hilbert变换。小波变换常应用于计算各个波段活动的能量(Mishra, Martínez, Schroeder, & Hillyard, 2012; Tallon-Baudry & Bertrand, 1999)。对相位的计算中, 则常会用到Hilbert变换(Canolty et al., 2006; Song, Meng, Chen, Zhou, & Luo, 2014)。窗口傅立叶变换也是一种时频分析算法, 但高频和低频波段信号的分辨率相同, 在心理学脑电数据分析中应用较少。传统的傅立叶变换则是一种频域分析方法, 不适合时变信号的分析。

2.1 傅立叶变换和窗口傅立叶变换

为了形象地说明几种时频分析方法的异同, 本文构造了两段简单的时变信号:第一段(图2A)的前半段是振幅为1的10 Hz正弦信号, 后半段是振幅为1.5的20 Hz余弦信号; 第二段(图2B)的前半段是振幅为1.5的20 Hz余弦信号, 后半段是振幅为1的10 Hz正弦信号。这两段不同时变信号的傅立叶变换结果完全相同(分别见图2C图2D)。由此可见, 傅立叶变换对于时变信号并不适用。在实际应用中, 傅立叶变换只适用于分析一些能产生相对稳恒的脑电振荡模式的实验数据, 如对稳态视诱发(SSVEP)的分析(Regan, 1966; Norcia, Appelbaum, Ales, Cottereau, & Rossion, 2015; Rossion, Prieto, Boremanse, Kuefner, & van Belle, 2012)。

图2

图2   时变信号的傅立叶变换示意图。A、B为信号示意图, 其中A为前半段为振幅为1的10 Hz正弦振荡, 后半段为振幅为1.5的20 Hz余弦振荡; B为前半段为振幅为1.5的20 Hz余弦振荡, 后半段为振幅为1的10 Hz正弦振荡。C、D分别为A、B信号的傅立叶变换结果。


窗口傅立叶变换, 又叫短时傅立叶变换(short- time Fourier transform, STFT, Allen, 1977), 是在傅立叶变换的基础上, 对算法做了改进, 从而适用于时变信号的分析。具体来说, 是对信号进行分析时, 加了一个短暂的时间窗口, 在窗口内对信号进行傅立叶变换。因为窗口时间短暂, 窗口内的信号可近似看作稳恒信号, 通过傅立叶变换就可以得到短暂窗口内的频率信息, 然后再将时间窗口依次施加于信号中不同的时间位置, 从而获得原信号不同时间位置的频率信息。用窗口傅立叶变换分析图2A、2B中的时变信号结果如图3A所示。

图3

图3   图2中的两个时变信号的时频分析示意图。A、B、C分别给出了窗口傅立叶变换、小波变换、Hilbert变换的分析结果。第一列、第二列分别为对图2A和图2B信号的分析结果。彩图见电子版, 下同。


窗口傅立叶变换中采用的窗口一般是Hanning窗、Hamming窗或者高斯窗口(Pampu, 2011)。选择了窗口后, 窗究表明, 低频波段相差数赫兹的频率可能代表了不同的心理功能。比如一项记忆研究中, 阿尔法波段(α, 8~12 Hz)与记忆的提取相关, 而西塔波段(θ, 4~8 Hz)与记忆编码相关(Klimesch, 1999), 两个波段频率只口长度在信号分析过程中便不再改变(如图5A), 于是高频和低频信号有相同的时间分辨率和频率分辨率。

脑电研相差数赫兹。对于较高频段, 往往较广频段范围内的信号代表了同一种心理功能, 比如伽马波段(γ, 30 Hz以上)往往是一大段频率具有同样的认知功能(Siegel, Donner, Oostenveld, Fries, & Engel, 2008)。这就需要在实际分析脑电信号时, 更加希望低频有更高的频率分辨率, 而高频则无需追求特别高的频率分辨率。要实现这样的目的, 需要采用小波变换。

2.2 小波变换

小波是一种时间长度有限并向两端快速衰减的振荡波形(Vidakovic & Mueller, 1991)。小波的振荡形态有很多种, 每一种形态被称为一种母小波 (Lee & Yamamoto, 1994)。构造母小波需满足一定的条件, 不同形态的母小波会对分析结果造成影响(Nobach et al., 2007)。所以分析数据时要根据所分析数据的特点, 选择合适的母小波(Kharate, Patil, & Bhale, 2007; Ngui, Leong, Hee, & Abdelrhman, 2013)。脑电研究中最常用的是复morlet小波分析(连续小波分析, Roach & Mathalon, 2008)。复Morlet小波可以看做是由余弦振荡为实部和正弦振荡为虚部构成的复振荡函数加一个高斯窗口组成, 正余弦振荡向窗口两端迅速衰减(Bernardino & Santos-Victor, 2005; Lieuw, 2015, 图4)。

图4

图4   复Morlet小波示意图, 实线是小波的实部, 虚线是小波的虚部。复Morlet小波的实部和虚部相位相差90度, 都可以看做是正余弦信号加了高斯窗口构成的向两端急剧衰减的振荡信号。


信号分析时, Morlet母小波经过压缩和扩张可生成一个小波族(wavelet family, 图5B), 不同压缩和扩张程度的小波可以理解为代表着不同“频率”的小波, 然后将小波族的子小波分别与信号做卷积, 从卷积结果中提取出信号的时频能量和相位。各个子小波具有不同的时间长度, “高频”小波的时间长度比“低频”小波更短, 但是两者在各自时间长度内的周期数是一样的。小波分析中低频波段时间窗口长, 频率窗口短, 因而可以得到较高的频率分辨率但时间分辨率较差; 而高频波段, 时间窗口短, 频率窗口长, 因而有较高的时间分辨率和较差的频率分辨率(图5B)。对图2的两个时变信号做小波变换结果如图3B所示。

图5

图5   窗口傅立叶变换和小波变换小波族(wavelet family)子小波对比。A, 窗口傅立叶变换的时间窗口长度固定, 对于高低频信号都有同样的时域和频域分辨率; B, 小波变换高低频的子小波有相同的周期数, 时间窗口长度并不相等。对于低频信号, 时间窗口更长, 时域分辨率更差, 频域分辨率更好; 高频信号则时间窗口短, 时域分辨率好, 频域分辨率差。


2.3 Hilbert变换

Hilbert变换是将信号与1/(pi×t)做卷积, 使得信号的相位旋转90度, 而本身的振幅大小不变(Nobach et al., 2007)。心理学领域的数据分析中, Hilbert变换的应用亦很广泛(Canolty et al., 2006; Penny, Duzel, Miller, & Ojemann, 2008; Song et al., 2014)。在EEG数据分析中, Hilbert变换主要是通过解析信号的构造来求解信号的能量和相位的。Hilbert变换常和滤波结合在一起用, 通过无相位偏差的带通滤波将要分析的信号限定在所要分析的较窄频段内, 然后再由Hilbert变换求解该频段信号的振幅和相位大小(Song et al., 2014)。一般认为, Hilbert变换拥有较高的频率分辨率, 而时间分辨率稍差, 在边界处的计算误差(边界效应)比较大。一些研究者采用了信号两端补零和加Hanning窗的办法减少Hilbert变换的边界效应(Song et al., 2014)。Hilbert变换对图2中的时变信号的分析结果如图3C所示。

总之, 窗口傅立叶变换、小波变换以及Hilbert变换都可以有效地分析时变信号(如图3所示)。有研究者对比了窗口傅立叶变换、小波变换以及Hilbert变换对一批脑电数据分析的结果, 发现在三种方法之间并没有实质的差别 (Bruns, 2004)。

3 时频分析的基本计算指标和心理学意义

时频分析技术通过丰富的计算指标揭示脑电中非相位锁定成分的心理意义。不同计算指标帮助揭示认知加工过程的不同方面。这部分内容将简要介绍时频分析用于脑电分析的三大类指标:能量, 相位一致性以及耦合。

3.1 能量(Power)

3.1.1 计算原理

能量是指信号分析结果中某频率成分在某时刻点的能量值, 代表了该频率成分振荡强度(振幅亦可以代表振荡强度, 为能量的平方根)。以小波变换求能量为例, 分析能量前, 首先构造合适的母小波, 母小波由小波核心参数Q值决定。感兴趣的频段为低频时, Q一般选为5, 或者3 (Mishra et al., 2012); 若感兴趣的频段为高频时, Q可以选择为6或者7。也有文献提出可以调节Q值的小波变换(Selesnick, 2011)。母小波构造好后, 根据要分析的频段确定具体频率值和对应尺度值, 然后根据尺度值构造子小波。将子小波和信号做卷积运算, 对卷积结果中的小波分析系数求模平方即为能量值, 而系数模则为振幅值。

3.1.2 能量的心理学意义

在数据分析过程中, 时频分析求能量有两种求解顺序, 分别对应不同的事件相关脑电活动(图6):一种是先对不同试次的信号叠加平均得到平均波形(ERP), 然后求平均波形的能量(evoked能量, 代表相位锁定于刺激发生时刻的能量); 另一种是求单个试次的能量, 然后把不同试次的相应时频能量进行叠加平均(包含了evoked能量和induced能量)。由此可见, 时频分析比传统的时域ERP分析方法可以挖掘出更多的事件诱发脑活动, 弥补了传统方法的局限。在Tallon-Baudry和Bertrand (1999)的研究中, induced γ活动代表了被试对三角形的整体觉知, 代表了一种对特征的整合, 这是evoked能量所无法揭示出来的。后来的研究者使用了不同的刺激, 仍然得到了相似的结论, 刺激之后200 ms后出现的非相锁的induced γ活动标志着物体的特征整合在一起(Busch, Herrmann, Müller, Lenz, & Gruber, 2006; Goffaux, Mouraux, Desmet, & Rossion, 2004; Gruber, Trujillo-Barreto, Giabbiconi, Valdés-Sosa, & Müller, 2006; Martinovic, Gruber, & Müller, 2007; Tallon- Baudry, 2009)。不仅如此, 也有研究发现γ能量在工作记忆中也标志着特征整合的过程(Morgan et al., 2011; Tseng, Chang, Chang, Liang, & Juan, 2016)。

图6

图6   计算evoked能量和induced能量示意图, A, 每个试次的γ振荡示意图, 蓝框内为evoked活动, 绿框内为induced活动; B, 波形叠加平均后的平均振荡波形, 只剩下了evoked活动; C, 每个试次的时频能量示意图, 可以看到每个试次的induced能量; D, 左侧evoked能量是平均波形时频分析的结果, 右侧induced能量是单个试次时频能量平均结果减去evoked能量的差异值。


虽然有研究表明, induced γ活动可能受到微眼动的影响(Yuval-Greenberg, Tomer, Keren, Nelken, & Deouell, 2008; Muthukumaraswamy, 2013), 但大量的研究在使用一些数据分析技术去除了微眼动对研究结果的影响后, 同样发现了特征捆绑过程中γ活动的存在(Hassler, Barreto, & Gruber, 2011; Keren, Yuval-Greenberg, & Deouell, 2010)。这说明了刺激出现200ms后持续较长时间的γ活动与特征捆绑相关, 这种γ活动代表了一种将大脑加工的特征信息整合起来的过程(Hassler, Friese, Martens, Trujillo-Barreto, & Gruber, 2013; Makin et al., 2011)。

α能量是较早被发现和研究最广泛的波段之一。α能量在清醒闭眼的时候比较显著, 是清醒时大脑后部区域能量最为显著的波段(Valipour, Shaligram, & Kulkarni, 2013)。大量研究发现α与注意密切相关, α能量低预示着注意状态(Sharma & Singh, 2015; Marshall, O’Shea, Jensen, & Bergmann, 2015; Capilla, Schoffelen, Paterson, Thut, & Gross, 2014; Händel, Haarmeier, & Jensen, 2011; Hanslmayr, Gross, Klimesch, & Shapiro, 2011; van Gerven & Jensen, 2009), 比如选择性注意的研究中发现了α能量的高低预示着注意选择过程。在使用Posner中央线索范式来研究空间选择性注意的文献中, 人们发现线索提示后约500ms (此时目标尚未出现), 会在提示空间对侧枕区皮层诱发比同侧更低的α能量, 代表了对线索提示位置的注意增强(Kelly, Gomez-Ramirez, & Foxe, 2009; Kelly, Lalor, Reilly, & Foxe, 2006; Rihs, Michel, & Thut, 2007; Sauseng et al., 2005; Thut, Nietzel, Brandt, & Pascual-Leone, 2006; Worden, Foxe, Wang, & Simpson, 2000)。不仅如此, 在一项外周Posner线索任务中, 无关声音刺激在左右视野空间出现, 也会引起目标出现前线索对侧大脑皮层枕区α能量的降低。说明了在外周Posner任务中, α能量的减小同样代表了被试对一侧空间的注意(StÖrmer, Feng, Martinez, McDonald, & Hillyard, 2016)。除此之外, 在基于特征的注意和基于物体的注意研究中都有类似的发现, 即特征加工相应脑区或者物体加工相应脑区α能量的降低代表了对该特征或物体的注意 (Snyder & Foxe, 2010; Knakker, Weiss, & Vidnyánszky, 2015; Fu et al., 2001)。在情绪性注意的研究中, 也有类似的发现, 即α能量大小预示着对情绪刺激的注意和抑制(Uusberg, Uibo, Kreegipuu, & Allik, 2013)。

定位于前额中区的θ能量则被发现与认知控制功能有关(Cavanagh, Cohen, & Allen, 2009; Cavanagh, Frank, Klein, & Allen, 2010; Womelsdorf, Johnston, Vinck, & Everling, 2010)。比如在2013年的一篇研究中, 研究者使用多任务的视觉游戏来训练老年人, 发现多任务视觉游戏训练后, 老年人额中区的θ能量以及额中区θ相位和枕区的θ相位联系增强, 说明了增加的额中区能量对视觉任务的完成有更好的认知控制作用(Anguera et al., 2013)。此外, 有研究发现β能量与运动密切相关(Engel & Fries, 2010; Bai et al., 2008; de Lange, Jensen, Bauer, & Toni, 2008; Waldert et al., 2008), 也有一些研究发现β能量与被试的注意有关(Gola, Magnuski, Szumska, & Wróbel, 2013; Deiber, Ibañez, Missonnier, Rodriguez, & Giannakopoulos, 2013)。

3.2 相位一致性(Phase Locking Index, PLI)

3.2.1 计算原理

相位一致性(PLI)是对某电极大量试次间相位一致性程度的衡量。相位一致性代表着大量试次在事件发生时刻以及后续较短时间内, 相位在试次间是否一致。如果相位完全一致, PLI为1, 相位完全随机, PLI则为0。计算时, 将不同试次的某时刻相位用单位向量表示, 然后计算不同试次间该向量的平均值, 原理如图7

图7

图7   PLI计算原理示意图, A, 单个试次某时刻某频率的能量和相位用复平面内向量表示, 圆代表单位圆, 向量在单位圆上的投影为只表示相位大小的单位向量; B, 若干试次的单位向量(黑色的点为单位向量的末端, 省去了箭头), 这些单位向量集中在第一象限, 平均向量(绿色)的大小(PLI)接近于1; C, 若干试次的单位向量平均分散在4个象限, 平均向量的大小(PLI)接近于0。


3.2.2 相位一致性的心理学意义

时域ERP成分和相位一致性有密切的关系(Busch, Dubois, & VanRullen, 2009; Mathewson, Gratton, Fabiani, Beck, & Ro, 2009)。相位一致性指标可以和能量指标结合起来, 用于探讨ERP产生的机制 (Makeig et al., 2002; Mishra et al., 2012)。如前所述, ERP成分有两种可能的产生机制:相位重置(图1C), 和新增成分(图1D)。一种区分两种机制的方法是检测刺激出现前后每个试次中的能量以及相位一致性; 如果前后能量没有明显不同, 但是平均结果中却有明显的ERP成分,那么便可以肯定相锁机制的存在; 如果刺激出现后单个试次的能量有明显增加, 且有中等强度的相位锁定性, 那么可能说明新增成分机制的存在。但在实际的研究中, ERP机制的分析还会受到α阻断效应的干扰(Mazaheri & Picton, 2005; Mishra et al., 2012), 增加了区分ERP机制的难度。大量研究者使用了更多的方法来尝试区分ERP产生的机制(Becker, Ritter, & Villringer, 2008; Mazaheri & Jensen, 2006)。其他脑功能成像技术也能从探讨ERP产生的机制中获得启发, Fell (2007)指出为了进一步理解事件相关 EEG和fMRI之间的关系, 将振幅和相位分离将是至关重要的步骤。

此外, 相位一致性还代表大脑认知加工过程的一些特点, 如有文献指出, α活动的相位一致性指标标志着α活动相对于刺激输入的一种初始化, 能够增强刺激随后加工的强度(Hebert, Lehmann, Tan, Travis, & Arenander, 2005)。海马中振荡活动的相位锁定性还和工作记表现有关(Kleen et al., 2016)。

3.3 耦合(Coherence)

耦合是时频分析应用到EEG数据分析领域最广泛的指标之一。耦合是计算不同电极间同步活动的指标, 反映了认知加工过程中的一种整体网络功能(Bowyer, 2016; Cabral, Kringelbach, & Deco, 2014; Greenblatt, Pflieger, & Ossadtchi, 2012)。有多种不同的耦合指标, 如同频率信号相位之间的耦合(即相位同步性), 不同频率振幅/能量之间的耦合, 低频信号相位和高频信号能量之间的耦合等。这些耦合指标分别反应不同的神经网络活动特点(Lachaux, Rodriguez, Martinerie, & Varela, 1999; Siegel et al., 2008; Canolty et al., 2006; Canolty & Knight, 2010; Jensen, Bonnefond, & VanRullen, 2012; Penny et al., 2008)。本部分内容主要介绍同频率信号不同电极间的相位同步性以及低频相位和高频能量之间耦合的计算原理和心理意义。

3.3.1 不同电极同频率信号的相位同步性(Phase Synchrony)的计算原理

大脑加工信息以一种网络化的方式进行, 一个直接的证据就是相距较远的脑部区域间的脑电信号存在耦合。远距离电极间同频率信号的相位同步变化被称为相位同步性(Phase Synchrony, Lachaux et al., 1999), 是两个电极同频率信号对应试次间相位差恒定性程度的衡量。对应试次间的相位差越稳定, 相位同步性越好, 反之, 相位同步性越差。计算时, 需要计算出两电极对应试次对应时刻某频率的相位值, 然后相减。将此相位差异用复平面内的单位向量表示, 不同试次之间的相位差对应的单位向量的末端分布在单位圆上。如果相位差异接近恒定, 那么这些单位向量的平均向量的长度便会接近于1; 反之, 相位差分布随机, 平均向量的长度便会接近于0。平均向量的长度就是两电极相位同步性的指标。计算原理如图8所示。

图8

图8   不同电极相位同步性计算原理示意图。A,上, 两个不同电极某试次某时刻各自的相位示意图, 下, 两个电极某试次某时刻的相位差; B,上, 两个不同电极另外一个试次某时刻(与A时刻相同)各自相位, 下, 两个电极该时刻相位差; C,左, 两电极不同试次某时刻相位差的分布靠近第一象限, 平均向量的模接近1, 右, 两电极不同试次某时刻相位差均匀分布, 平均向量的模接近于0。


3.3.2 相位同步性的心理学意义

相位同步性体现了长距离脑区的同步活动, 反映了自下而上或者自上而下的信息传递和相互控制。有研究指出无论休息状态还是任务状态, 不同波段的相位同步性普遍存在于脑电活动中(Pockett, Bold, & Freeman, 2009)。Siegel将长距离的相位同步性视为神经网络功能的一个标志(Siegel et al., 2008)。Finger的研究也表明, 相位同步性可能是大脑将分散于不同区域的信息整合在一起形成一个整体的主要机制(Finger & önig, 2014)。在一个需要左右大脑半球共同加工视觉信息形成整体知觉的研究中, 由小字母组成的大字母横跨左右视野, 在需要认出大字母的实验条件中, 左右半球电极间表现出了相位同步性的增加, 两个半球的信息得到了整合(Rose, Sommer, & Buchel, 2006)。老年人在多任务游戏中额中区和枕区的长距离θ相位耦合代表了高级脑区对低级脑区的认知控制(Anguera et al., 2013)。在关于注意的研究中, 发现不同脑区间的γ活动相位耦合与注意活动有关(Doesburg, Roggeveen, Kitajo, & Ward, 2008)。而一项研究则表明双任务中α活动相位同步性则是注意负载强弱的指示器(Kwon et al., 2015)。也有研究表明, α相位同步性与认知任务的表现有关(Palva & Palva, 2011)。而γ活动同步性则还与对物体的意识相关(Castelhano, Rebola, Leitão, Rodriguez, & Castelo-Branco, 2013)。在视觉短时记忆中, 也发现了纹外皮层的β活动同步性指标与短时记忆保持的相关 (Tallon-Baudry, Bertrand, & Fischer, 2001)。此外, 相位同步性也是标记一些疾病的重要指标, 可能暗示着病人的神经网络功能的紊乱。一些研究表明癫痫病人发病的时候相位同步性会有一个很大的增长 (Franaszczuk & Bergey, 1999), 另外的研究也表明精神分裂症病人的γ同步性指标异常(Spencer et al., 2003)。这些不同的研究都说明相位同步性体现了大脑的各功能区联合为网络而显现出更高级的认知功能, 可以帮助人们完成复杂的认知任务。

3.3.3 低频信号相位和高频信号能量之间的耦合(Phase-Amplitude Coupling, PAC)的计算原理

有研究指出, 长距离脑区间的调控多借助于低频振荡, 而高频信号往往是局部活动的一种体现(Canolty & Knight, 2010), 低频和高频之间的耦合就具有了特殊的功能意义, 局部脑加工体现局部的大脑的模块化加工, 而与长距离的低频信号的耦合代表一种网络的信息整合。这种耦合在数据中表现为高频能量的高低起伏有一定的周期性, 与信号中低频成分的相位变化相关(图9A)。

图9

图9   低频相位和高频能量耦合以及计算方法示意图。A为耦合示意图, 表现为高频能量以一定的低频周期振荡变化。B、C、D为耦合计算原理示意图, B为某个时刻高频能量和低频相位组成的复数在复平面上对应一个点; C为许多时刻点的分布, 偏向第一象限, 平均复向量指向第一象限; D为许多时刻点的分布, 在四个象限均匀分布, 平均向量长度很小, 几乎为0。


这种耦合的计算方法有很多(Penny et al., 2008; Samiee & Baillet, 2017), 其中一些研究给出了一种相对灵敏的计算方法(Canolty et al., 2006; Song et al., 2014)。首先, 计算出原信号中的高频信号(如γ)的振幅ampγ(t)和低频信号(如θ)的相位phaseθ(t), 每个对应时刻的高频能量和低频相位组成的复数ampγ(t)•ephaseθ(t), 在复平面内对应一个点(图9B), 许多个时刻的点构成了一个分布(图9C和9D), 如果复数点的分布偏向于一个象限, 那么就说明高频能量多分布在某一个相位角内, 两者有耦合(图9C), 否则低频相位和高频能量则无耦合(图9D)。实际计算时, 还需要用置换检验(permutation test)的方法衡量耦合的相对强度(Canolty et al., 2006):

3.3.4 低频相位高频能量耦合的心理学意义

低频相位和高频能量的耦合反映了多种认知机能, 在不同的认知任务中都可能有相应表现。该耦合可能反映了这样的机制, 将信息从大的行为反应时间尺度转移到局部的尺度, 进行进一步的运算和突触修饰, 从而整合多个时空尺度功能(Canolty & Knight, 2010; Jensen et al., 2012)。Bonnefond等人关于α相位和γ能量的耦合研究说明信息自上而下的门控机制, 局部的信息计算被约100 ms的时间周期门控控制, 提供了周期性抑制的控制作用(Bonnefond & Jensen, 2015); 而在另外一项研究中, 研究者使用了不同的行为任务, 发现低频θ相位和高频γ能量的耦合模式直接和行为任务有关, 在不同的行为任务中, 表现出不同脑区间的耦合, 说明进行不同任务时不同大脑区域之间的信息交流(Canolty et al., 2006)。认知任务中, 高级脑区对低级脑区的认知控制可以体现在高级脑区的低频振荡活动相位与低级脑区的高频振荡能量的耦合上(Helfrich & Knight, 2016)。不仅如此, 在工作记忆任务中, 长距离的低频相位(θ、α)和高频能量(β、γ)耦合以及长距离的低频相位的耦合反映了不同脑区之间的信息交流(Daume, Gruber, Engel, & Friese, 2017)。在一篇关于儿童工作记忆的研究中, 研究者通过对儿童工作记忆的训练发现认知控制区域(额顶区)的α相位和低级脑区(颞区)的γ活动的耦合增强, 提示了该类型的耦合在工作记忆中的作用(Barnes, Nobre, Woolrich, Baker, & Astle, 2016)。动物研究也表明海马记录到的低频θ和高频γ的耦合与学习和完成回忆任务有关(Tort, Komorowski, Manns, Kopell, & Eichenbaum, 2009)。还有文献指出低频相位和高频能量耦合在记忆中标志着编码的功能(Friese et al., 2013)。工作记忆中(短时记忆)的信息编码以及容量也可以用这类耦合解释(Axmacher et al., 2010)。最近的一项研究中, 发现了慢波睡眠阶段的慢波振荡活动的相位与纺锤波之间的耦合在睡眠记忆巩固中有重要的作用, 可以部分解释老年人记忆巩固能力的衰退(Helfrich, Mander, Jagust, Knight, & Walker, 2018)。

总的来说, 低频相位和高频能量耦合是当前研究的一个热点, 在不同任务以及不同状态都可以发现这样的活动模式, 说明这种耦合反映了脑内不同大脑区域之间的信息交流以及信息控制, 从而帮助被试完成各种高级认知任务, 反映了大脑加工信息时的网络功能。

4 小结

时频分析是当前心理学脑电领域的一个热点算法应用。它可以帮助我们捕捉脑电信号中非相位锁定于事件发生时刻的活动, 可以将事件诱发脑活动的探索拓展到刺激出现前。心理学脑电数据分析领域应用最多的时频分析算法是复Morlet小波变换和Hilbert变换。时频分析在脑电数据分析领域的应用主要体现在一些指标上。不同频段的能量指标反映了不同的认知过程, 如α能量与选择性注意密切相关, 而γ能量则与特征捆绑密切相关。相位一致性指标往往被用来探讨时域ERP成分产生的机制。耦合往往反映了大脑作为一个网络体现出来的整体功能, 同频率远距离脑区之间的相位耦合和低频相位和高频能量的耦合是过去研究中的两个热点。未来可以关注时频分析技术在非任务态(静息态)中的应用以及其他耦合指标(如不同频段能量之间, 不同频段相位之间)在探索心理过程中的应用。

The authors have declared that no competing interests exist.
作者已声明无竞争性利益关系。

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Recent findings indicate that the hippocampus supports not only longterm memory encoding but also plays a role in working memory (WM) maintenance of multiple items; however, the neural mechanism underlying multi-item maintenance is still unclear. Theoretical work suggests that multiple items are being maintained by neural assemblies synchronized in the gamma frequency range (25-100 Hz) that are locked to consecutive phase ranges of oscillatory activity in the theta frequency range (4-8 Hz). Indeed, cross-frequency coupling of the amplitude of high-frequency activity to the phase of slower oscillations has been described both in animals and in humans, but has never been linked to a theoretical model of a cognitive process. Here we used intracranial EEG recordings in human epilepsy patients to test pivotal predictions from theoretical work. First we show that simultaneous maintenance of multiple items in WM is accompanied by cross-frequency coupling of oscillatory activity in the hippocampus, which is recruited during multiitem WM. Second, maintenance of an increasing number of items is associated with modulation of beta/gamma amplitude with theta band activity of lower frequency, consistent with the idea that longer cycles are required for an increased number of representations by gamma cycles. This effect cannot be explained by a difference in theta or beta/gamma power. Third, we describe how the precision of cross-frequency coupling predicts individual WM performance. These data support the idea that working memory in humans depends on a neural code using phase information.

Bai O., Lin P., Vorbach S., Floeter M. K., Hattori N., & Hallett M . ( 2008).

A high performance sensorimotor beta rhythm-based brain-computer interface associated with human natural motor behavior

Journal of Neural Engineering, 5( 1), 24-35.

URL     [本文引用: 1]

Barnes J. J., Nobre A. C., Woolrich M. W., Baker K., & Astle D. E . ( 2016).

Training working memory in childhood enhances coupling between frontoparietal control network and task-related regions

Journal of Neuroscience, 36( 34), 9001-9011.

URL     [本文引用: 1]

Bastiaansen M., Mazaheri A., & Jensen, O .( 2012).

Beyond ERPs: Oscillatory neuronal dynamics

In S J Luck & E S Kappenman (Eds), The Oxford handbook of event-related potential components (pp 31-50). New York: NY: Oxford University Press.

URL     [本文引用: 1]

In this study, we find numerical solution of by Newton Interpolation. We determined coefficients such that to be an approximation for u (x). This method give an approximate solution for integral equation and also it is powerful in solving both Fredholm and Volterra integral equations, specially for the first kind. In this study, we use special interpolation and quadrature rule for numerical integration. Effectiveness and accuracy of new method are presented with numerical examples.

Becker R., Ritter P., & Villringer A . ( 2008).

Influence of ongoing alpha rhythm on the visual evoked potential

Neuroimage, 39( 2), 707-716.

URL     PMID:17977023      [本文引用: 1]

The relationship between ongoing occipital alpha rhythm (8–12 Hz) and the generation of visual evoked potentials (VEPs) has been discussed controversially. While the “evoked theory” sees no interaction between VEP generation and the alpha rhythm, the “oscillatory theory” (also known as “phase-reset theory”) postulates VEP generation to be based on alpha rhythm phase resetting. Previous experimental results are contradictory, rendering a straightforward interpretation difficult. Our approach was to theoretically model the implications of the evoked and oscillatory theory also incorporating stimulus-induced alpha-rhythm desynchronization. As a result, the model based on the oscillatory theory predicts alpha-band dependent VEP amplitudes but constant phase locking. The model based on the evoked theory predicts unaffected VEP amplitudes but alpha-band dependent phase locking. Subsequently, we analyzed experimental data in which VEPs were assessed in an “eyes open” and “eyes closed” condition in 17 subjects. For early components of the VEP, findings are in agreement with the evoked theory, i.e. VEP amplitudes remain unaffected and phase locking decreases during periods of high alpha activity. Late VEP component amplitudes (>02175 ms), however, are dependent on pre-stimulus alpha amplitudes. This interaction is contradictory to the oscillatory theory since this VEP amplitude difference is not paralleled by a corresponding difference in alpha-band amplitude in the affected time window. In summary, by using a model-based approach we identified early VEPs to be compatible with the evoked theory, while results of late VEPs support a modulatory but not causative role – the latter implied by the oscillatory theory – of alpha activity for EP generation.

Berger, H . ( 1929).

über das Elektrenkephalogramm des Menschen

Archiv für Psychiatrie und Nervenkrankheiten, 87, 527-570.

URL     [本文引用: 1]

Published in print

Bernardino, A., & Santos-Victor, J . ( 2005).

A real-time gabor primal sketch for visual attention

In J. S. Marques, N. Pérez de la Blanca, & P. Pina (Eds.), Pattern recognition and image analysis. IbPRIA 2005. Lecture notes in computer science, vol 3522( pp. 335-342). Berlin, Heidelberg: Springer.

[本文引用: 1]

Bonnefond, M., & Jensen, O . ( 2015).

Gamma activity coupled to alpha phase as a mechanism for top-down controlled gating

PLoS One, 10( 6), e0128667.

URL     PMID:26039691      [本文引用: 1]

Coupling between neural oscillations in different frequency bands has been proposed to coordinate neural processing. In particular, gamma power coupled to alpha phase is proposed to reflect gating of information in the visual system but the existence of such a mechanism remains untested. Here, we recorded ongoing brain activity using magnetoencephalography in subjects who performed a modified Sternberg working memory task in which distractors were presented in the retention interval. During the anticipatory pre-distractor period, we show that the phase of alpha oscillations was coupled with the power of high (80-120Hz) gamma band activity, i.e. gamma power consistently was lower at the trough than at the peak of the alpha cycle (9-12Hz). We further show that high alpha power was associated with weaker gamma power at the trough of the alpha cycle. This result is in line with alpha activity in sensory region implementing a mechanism of pulsed inhibition silencing neuronal firing every ~100 ms.

Bowyer, S. M . ( 2016).

Coherence a measure of the brain networks: Past and present

Neuropsychiatric Electrophysiology, 2, 1.

URL     [本文引用: 1]

AbstractBrian connectivity describes the networks of functional and anatomical connections across the brain. The functional network communications across the brain networks dependent on neuronal oscillations. Detection of the synchronous activation of neurons can be used to determine the wellbeing or integrity of the functional connectivity in the human brain networks. Well-connected highly synchronous functional activity can be measured by Electroencephalography (EEG) or Magnetoencephalography (MEG) and then analyzed with several types of mathematical algorithms. Coherence is one mathematical method that can be used to determine if two or more sensors, or brain regions, have similar neuronal oscillatory activity with each other. Since the 1960 , coherence has generally been assessed on the similarity of the frequency content across EEG sensors. Recently coherence, after it has been imaged in the brain, has been used to assess how coherent or connected specific locations in the brain are networked together in several different neurological disorders. Statistical analysis can then be performed on the coherence results to verify evidence of normal or abnormal network activity in a patient. In this review we highlight how functional brain connectivity is assessed in Source space using coherence technique measured by MEG.

Bruns, A . ( 2004).

Fourier-, Hilbert- and wavelet-based signal analysis: Are they really different approaches?

Journal of Neuroscience Methods, 137( 2), 321-332.

URL     PMID:15262077      [本文引用: 1]

Spectral signal analysis constitutes one of the most important and most commonly used analytical tools for the evaluation of neurophysiological signals. It is not only the spectral parameters per se (amplitude and phase) which are of interest, but there is also a variety of measures derived from them, including important coupling measures like coherence or phase synchrony. After reviewing some of these measures in order to underline the widespread relevance of spectral analysis, this report compares the three classical spectral analysis approaches: Fourier, Hilbert and wavelet transform. Recently, there seems to be increasing acceptance of the notion that Hilbert- or wavelet-based analyses be in some way superior to Fourier-based analyses. The present article counters such views by demonstrating that the three techniques are in fact formally (i.e. mathematically) equivalent when using the class of wavelets that is typically applied in spectral analyses. Moreover, spectral amplitude serves as an example to show that Fourier, Hilbert and wavelet analysis also yield equivalent results in practical applications to neuronal signals.

Busch N. A., Dubois, J. & VanRullen, R .( 2009).

The phase of ongoing EEG oscillations predicts visual perception

Journal of Neuroscience, 29( 24), 7869-7876.

URL     [本文引用: 1]

Busch N. A., Herrmann C. S., Müller M. M., Lenz D., & Gruber T . ( 2006).

A cross-laboratory study of event- related gamma activity in a standard object recognition paradigm

Neuroimage, 33( 4), 1169-1177.

URL     [本文引用: 1]

Cabral J., Kringelbach M. L., & Deco G . ( 2014).

Exploring the network dynamics underlying brain activity during rest

Progress in Neurobiology, 114, 102-131.

URL     PMID:24389385      [本文引用: 1]

Since the mid 1990s, the intriguing dynamics of the brain at rest has been attracting a growing body of research in neuroscience. Neuroimaging studies have revealed distinct functional networks that slowly activate and deactivate, pointing to the existence of an underlying network dynamics emerging spontaneously during rest, with specific spatial, temporal and spectral characteristics. Several theoretical scenarios have been proposed and tested with the use of large-scale computational models of coupled brain areas. However, a mechanistic explanation that encompasses all the phenomena observed in the brain during rest is still to come. In this review, we provide an overview of the key findings of resting-state activity covering a range of neuroimaging modalities including fMRI, EEG and MEG. We describe how to best define and analyze anatomical and functional brain networks and how unbalancing these networks may lead to problems with mental health. Finally, we review existing large-scale models of resting-state dynamics in health and disease. An important common feature of resting-state models is that the emergence of resting-state functional networks is obtained when the model parameters are such that the system operates at the edge of a bifurcation. At this critical working point, the global network dynamics reveals correlation patterns that are spatially shaped by the underlying anatomical structure, leading to an optimal fit with the empirical BOLD functional connectivity. However, new insights coming from recent studies, including faster oscillatory dynamics and non-stationary functional connectivity, must be taken into account in future models to fully understand the network mechanisms leading to the resting-state activity.

Canolty R. T., Edwards E., Dalal S. S., Soltani M., Nagarajan S. S., Kirsch H. E., .. Knight R. T . ( 2006).

High gamma power is phase-locked to theta oscillations in human neocortex

Science, 313( 5793), 1626-1628.

URL     PMID:16973878      [本文引用: 6]

We observed robust coupling between the high- and low-frequency bands of ongoing electrical activity in the human brain. In particular, the phase of the low-frequency theta (4 to 8 hertz) rhythm modulates power in the high gamma (80 to 150 hertz) band of the electrocorticogram, with stronger modulation occurring at higher theta amplitudes. Furthermore, different behavioral tasks evoke distinct patterns of theta/high gamma coupling across the cortex. The results indicate that transient coupling between low- and high-frequency brain rhythms coordinates activity in distributed cortical areas, providing a mechanism for effective communication during cognitive processing in humans.

Canolty, R. T., & Knight, R. T . ( 2010).

The functional role of cross-frequency coupling

Trends in Cognitive Sciences, 14( 11), 506-515.

URL     PMID:20932795      [本文引用: 3]

Abstract Recent studies suggest that cross-frequency coupling (CFC) might play a functional role in neuronal computation, communication and learning. In particular, the strength of phase-amplitude CFC differs across brain areas in a task-relevant manner, changes quickly in response to sensory, motor and cognitive events, and correlates with performance in learning tasks. Importantly, whereas high-frequency brain activity reflects local domains of cortical processing, low-frequency brain rhythms are dynamically entrained across distributed brain regions by both external sensory input and internal cognitive events. CFC might thus serve as a mechanism to transfer information from large-scale brain networks operating at behavioral timescales to the fast, local cortical processing required for effective computation and synaptic modification, thus integrating functional systems across multiple spatiotemporal scales. Copyright 漏 2010 Elsevier Ltd. All rights reserved.

Capilla A., Schoffelen J. M., Paterson G., Thut G., & Gross J . ( 2014).

Dissociated α-band modulations in the dorsal and ventral visual pathways in visuospatial attention and perception

Cerebral Cortex, 24, 550-561.

URL     [本文引用: 1]

Castelhano J., Rebola J., Leitão B., Rodriguez E., & Castelo-Branco M . ( 2013).

To perceive or not perceive: The role of gamma-band activity in signaling object percepts

PLoS One, 8( 6), e66363.

URL     PMID:23785494      [本文引用: 1]

The relation of gamma-band synchrony to holistic perception in which concerns the effects of sensory processing, high level perceptual gestalt formation, motor planning and response is still controversial. To provide a more direct link to emergent perceptual states we have used holistic EEG/ERP paradigms where the moment of perceptual "discovery" of a global pattern was variable. Using a rapid visual presentation of short-lived Mooney objects we found an increase of gamma-band activity locked to perceptual events. Additional experiments using dynamic Mooney stimuli showed that gamma activity increases well before the report of an emergent holistic percept. To confirm these findings in a data driven manner we have further used a support vector machine classification approach to distinguish between perceptual vs. non perceptual states, based on time-frequency features. Sensitivity, specificity and accuracy were all above 95%. Modulations in the 30-75 Hz range were larger for perception states. Interestingly, phase synchrony was larger for perception states for high frequency bands. By focusing on global gestalt mechanisms instead of local processing we conclude that gamma-band activity and synchrony provide a signature of holistic perceptual states of variable onset, which are separable from sensory and motor processing.

Cavanagh J. F., Cohen M. X., & Allen, J. J. B . ( 2009).

Prelude to and resolution of an error: EEG phase synchrony reveals cognitive control dynamics during action monitoring

Journal of Neuroscience, 29( 1), 98-105.

URL     [本文引用: 1]

Cavanagh J. F., Frank M. J., Klein T. J., & Allen, J. J. B . ( 2010).

Frontal theta links prediction errors to behavioral adaptation in reinforcement learning

NeuroImage, 49( 4), 3198-3209.

URL     PMID:19969093      [本文引用: 1]

Abstract Investigations into action monitoring have consistently detailed a frontocentral voltage deflection in the event-related potential (ERP) following the presentation of negatively valenced feedback, sometimes termed the feedback-related negativity (FRN). The FRN has been proposed to reflect a neural response to prediction errors during reinforcement learning, yet the single-trial relationship between neural activity and the quanta of expectation violation remains untested. Although ERP methods are not well suited to single-trial analyses, the FRN has been associated with theta band oscillatory perturbations in the medial prefrontal cortex. Mediofrontal theta oscillations have been previously associated with expectation violation and behavioral adaptation and are well suited to single-trial analysis. Here, we recorded EEG activity during a probabilistic reinforcement learning task and fit the performance data to an abstract computational model (Q-learning) for calculation of single-trial reward prediction errors. Single-trial theta oscillatory activities following feedback were investigated within the context of expectation (prediction error) and adaptation (subsequent reaction time change). Results indicate that interactive medial and lateral frontal theta activities reflect the degree of negative and positive reward prediction error in the service of behavioral adaptation. These different brain areas use prediction error calculations for different behavioral adaptations, with medial frontal theta reflecting the utilization of prediction errors for reaction time slowing (specifically following errors), but lateral frontal theta reflecting prediction errors leading to working memory-related reaction time speeding for the correct choice. Copyright 2009 Elsevier Inc. All rights reserved.

Daume J., Gruber T., Engel A. K., & Friese U . ( 2017).

Phase-amplitude coupling and long-range phase synchronization reveal frontotemporal interactions during visual working memory

Journal of Neuroscience, 37( 2), 313-322.

URL     [本文引用: 1]

de Lange F. P., Jensen O., Bauer M., & Toni I . ( 2008).

Interactions between posterior gamma and frontal alpha/beta oscillations during imagined actions

Frontiers in Human Neuroscience, 2, 7.

[本文引用: 1]

Deiber M. P., Ibañez V., Missonnier P., Rodriguez C., & Giannakopoulos P . ( 2013).

Age-associated modulations of cerebral oscillatory patterns related to attention control

NeuroImage, 82, 531-546.

URL     PMID:23777759      [本文引用: 1]

61Age affects brain rhythms in temporo-spatial attention and conflict resolution.61Posterior attention-related alpha activation declines with age.61Older adults engage motor-related structures through activation in the beta band.61Conflict resolution is slowed by over-engagement of alpha attentional resources.

Doesburg S. M., Roggeveen A. B., Kitajo K., & Ward L. M . ( 2008).

Large-scale gamma-band phase synchronization and selective attention

Cerebral Cortex, 18( 2), 386-396.

URL     PMID:17556771      [本文引用: 1]

Abstract Explaining the emergence of a coherent conscious percept and an intentional agent from the activity of distributed neurons is key to understanding how the brain produces higher cognitive processes. Gamma-band synchronization has been proposed to be a mechanism for the functional integration of neural populations that together form a transitory, large-scale, task- and/or percept-specific network. The operation of this mechanism in the context of attention orienting entails that cortical regions representing attended locations should show more gamma-band synchronization with other cortical areas than would those representing unattended locations. This increased synchronization should be apparent in the same time frame as that of the deployment of attention to a particular location. In order to observe this effect, we made electroencephalogram recordings while subjects attended to one side or the other of the visual field (which we confirmed by event-related potential analysis) and calculated phase-locking statistics between the signals recorded at relevant electrode pairs. We observed increased gamma-band phase synchronization between visual cortex contralateral to the attended location and other, widespread, cortical areas approximately 240-380 ms after the directional cue was presented, confirming the prediction of a large-scale gamma synchronous network oriented to the cued location.

Engel, A. K., & Fries, P . ( 2010).

Beta-band oscillations--signalling the status quo?

Current Opinion in Neurobiology, 20( 2), 156-165.

URL     [本文引用: 1]

Fell, J . ( 2007).

Cognitive neurophysiology: Beyond averaging

NeuroImage, 37( 4), 1069-1072.

URL    

Finger, H., & önig, P . ( 2014).

Phase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network

Frontiers in Computational Neuroscience, 7, 195.

[本文引用: 1]

Franaszczuk, P. J., & Bergey, G. K . ( 1999).

An autoregressive method for the measurement of synchronization of interictal and ictal EEG signals

Biological Cybernetics, 81, 3-9.

URL     [本文引用: 1]

Friese U., öster M., Hassler U., Martens U., Trujillo- Barreto N., & Gruber T . ( 2013).

Successful memory encoding is associated with increased cross-frequency coupling between frontal theta and posterior gamma oscillations in human scalp-recorded EEG

NeuroImage, 66, 642-647.

URL     [本文引用: 1]

Fu K.-M. G., Foxe J. J., Murray M. M., Higgins B. A., Javitt D. C., & Schroeder C. E . ( 2001).

Attention- dependent suppression of distracter visual input can be cross-modally cued as indexed by anticipatory parieto- occipital alpha-band oscillations

Cognitive Brain Research, 12, 145-152.

URL     [本文引用: 1]

Goffaux V., Mouraux A., Desmet S., & Rossion B . ( 2004).

Human non-phase-locked gamma oscillations in experience- based perception of visual scenes

Neuroscience Letters, 354( 1), 14-17.

URL     PMID:14698471      [本文引用: 1]

Perception results from ongoing interactions between stimulus-driven visual processes and cognitive context. These reciprocal relations are emphasized when a visual stimulus is degraded, forcing the perceiver to fill the missing information in, based on internal representations. The neural mechanisms by which internal representations facilitate visual perception are still unclear. Here we investigated the role of EEG oscillations in the gamma band, thought to reflect the elaboration of internal visual representations, in the experience-based perception of visual scenes. Twelve subjects were trained with degraded images of natural scenes. EEG was recorded while they performed a detection task on trained and untrained degraded stimuli. Non-phase-locked gamma band responses in a large frequency spectrum (55 85 Hz) were observed around 200 ms post-stimulus onset at posterior sites, and were larger when subjects reported an accurate perception based on previous experience. These results suggest that mid-latency gamma oscillations in the visual cortex underlie the experience-based perception of visual scenes.

Gola M., Magnuski M., Szumska I., & Wróbel A . ( 2013).

EEG beta band activity is related to attention and attentional deficits in the visual performance of elderly subjects

International Journal of Psychophysiology, 89( 3), 334-341.

URL     [本文引用: 1]

Greenblatt R. E., Pflieger M. E., & Ossadtchi A. E . ( 2012).

Connectivity measures applied to human brain electrophysiological data

Journal of Neuroscience Methods, 207( 1), 1-16.

URL     PMID:22426415      [本文引用: 1]

Abstract Connectivity measures are (typically bivariate) statistical measures that may be used to estimate interactions between brain regions from electrophysiological data. We review both formal and informal descriptions of a range of such measures, suitable for the analysis of human brain electrophysiological data, principally electro- and magnetoencephalography. Methods are described in the space-time, space-frequency, and space-time-frequency domains. Signal processing and information theoretic measures are considered, and linear and nonlinear methods are distinguished. A novel set of cross-time-frequency measures is introduced, including a cross-time-frequency phase synchronization measure. Copyright 2012 Elsevier B.V. All rights reserved.

Gruber, T., & Müller, M. M . ( 2005).

Oscillatory brain activity dissociates between associative stimulus content in a repetition priming task in the human EEG

Cerebral Cortex, 15( 1), 109-116.

[本文引用: 1]

Gruber T., Trujillo-Barreto N. J., Giabbiconi C. M., Valdés-Sosa P. A., & Müller M. M . ( 2006).

Brain electrical tomography (BET) analysis of induced gamma band responses during a simple object recognition task

NeuroImage, 29( 3), 888-900.

URL     [本文引用: 1]

Händel B. F., Haarmeier T., & Jensen O . ( 2011).

Alpha oscillations correlate with the successful inhibition of unattended stimuli

Journal of Cognitive Neuroscience, 23, 2494-2502.

URL     [本文引用: 1]

Hanslmayr S., Gross J., Klimesch W., & Shapiro K. L . ( 2011).

The role of alpha oscillations in temporal attention

Brain Research Reviews, 67, 331-343.

URL     PMID:21592583      [本文引用: 1]

78 Alpha oscillations indicate internally and externally oriented brain states. 78 Different parameters of alpha oscillatory activity predict visual perception. 78 A neuro-cognitive model is presented to explain the attentional blink.

Hassler U., Barreto N. T., & Gruber T . ( 2011).

Induced gamma band responses in human EEG after the control of miniature saccadic artifacts

NeuroImage, 57( 4), 1411-1421.

URL     PMID:21645624      [本文引用: 1]

78An algorithm for the correction of saccade-related transient potentials (COSTRAP). 78Artifact-controlled gamma band oscillations are of cortical origin. 78Induced gamma band responses are sensitive to object recognition.

Hassler U., Friese U., Martens U., Trujillo-Barreto N., & Gruber T . ( 2013).

Repetition priming effects dissociate between miniature eye movements and induced gamma- band responses in the human electroencephalogram

European Journal of Neuroscience, 38( 3), 2425-2433.

URL     [本文引用: 1]

Hebert R., Lehmann D., Tan G., Travis F., & Arenander A . ( 2005).

Enhanced EEG alpha time-domain phase synchrony during Transcendental Meditation: Implications for cortical integration theory

Signal Processing, 85( 11), 2213-2232.

URL     [本文引用: 1]

Helfrich R. F., & Knight, R. T . ( 2016).

Oscillatory dynamics of prefrontal cognitive control

Trends in Cognitive Sciences, 20( 12), 916-930.

URL     PMID:5127407      [本文引用: 1]

The prefrontal cortex (PFC) provides the structural basis for numerous higher cognitive functions. However, it is still largely unknown which mechanisms provide the functional basis for flexible cognitive control of goal-directed behavior. Here, we review recent findings, which suggest that the functional architecture of cognition is profoundly rhythmic and propose that the PFC serves as a conductor to orchestrate task-relevant large-scale networks. We highlight several studies that demonstrated that oscillatory dynamics, such as phase resetting, cross-frequency coupling and entrainment, support PFC-dependent recruitment of task-relevant regions into coherent functional networks. Importantly, these findings support the notion that distinct spectral signatures reflect different cortical computations supporting effective multiplexing on different temporal channels along the same anatomical pathways.

Helfrich R. F., Mander B. A., Jagust W. J., Knight R. T., & Walker M. P . ( 2018).

Old brains come uncoupled in sleep: Slow wave-spindle synchrony, brain atrophy, and forgetting

Neuron, 97( 1), 221-230. e4.

URL     PMID:29249289      [本文引用: 1]

Abstract The coupled interaction between slow-wave oscillations and sleep spindles during non-rapid-eye-movement (NREM) sleep has been proposed to support memory consolidation. However, little evidence in humans supports this theory. Moreover, whether such dynamic coupling is impaired as a consequence of brain aging in later life, contributing to cognitive and memory decline, is unknown. Combining electroencephalography (EEG), structural MRI, and sleep-dependent memory assessment, we addressed these questions in cognitively normal young and older adults. Directional cross-frequency coupling analyses demonstrated that the slow wave governs a precise temporal coordination of sleep spindles, the quality of which predicts overnight memory retention. Moreover, selective atrophy within the medial frontal cortex in older adults predicted a temporal dispersion of this slow wave-spindle coupling, impairing overnight memory consolidation and leading to forgetting. Prefrontal-dependent deficits in the spatiotemporal coordination of NREM sleep oscillations therefore represent one pathway explaining age-related memory decline.

Huang N. E., Shen Z., Long S. R., Wu M. C., Shin H. H., Zheng Q., .. Liu H. H . ( 1998).

The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454, 903-995.

URL     [本文引用: 1]

Ijspeert, A. J . ( 2008).

Central pattern generators for locomotion control in animals and robots: A review

Neural Networks, 21( 4), 642-653.

URL     PMID:18555958      [本文引用: 1]

Abstract The problem of controlling locomotion is an area in which neuroscience and robotics can fruitfully interact. In this article, I will review research carried out on locomotor central pattern generators (CPGs), i.e. neural circuits capable of producing coordinated patterns of high-dimensional rhythmic output signals while receiving only simple, low-dimensional, input signals. The review will first cover neurobiological observations concerning locomotor CPGs and their numerical modelling, with a special focus on vertebrates. It will then cover how CPG models implemented as neural networks or systems of coupled oscillators can be used in robotics for controlling the locomotion of articulated robots. The review also presents how robots can be used as scientific tools to obtain a better understanding of the functioning of biological CPGs. Finally, various methods for designing CPGs to control specific modes of locomotion will be briefly reviewed. In this process, I will discuss different types of CPG models, the pros and cons of using CPGs with robots, and the pros and cons of using robots as scientific tools. Open research topics both in biology and in robotics will also be discussed.

Jensen O., Bonnefond M., & VanRullen R . ( 2012).

An oscillatory mechanism for prioritizing salient unattended stimuli

Trends in Cognitive Sciences, 16( 4), 200-206.

URL     PMID:22436764      [本文引用: 2]

To survive in a complex world, it is important that unattended, but salient, input can still draw one's attention. In this article, we suggest that posterior alpha oscillations (8–13Hz) provide a mechanism for prioritizing and ordering unattended visual input according to ‘relevance’. Gamma oscillations (30–100Hz) that are phase-locked to the alpha oscillations keep competing unattended representations apart in time, thus creating a sequence of perceptual cycles. As inhibition gradually lowers within an alpha cycle, the ordered sequence of competing input is activated, producing a temporal phase code for saliency. The proposed mechanism is based on recent experiments indicating that the phase of alpha activity modulates perception and that alpha oscillations are produced by periodic pulses of inhibition.

Kelly S. P., Gomez-Ramirez M., & Foxe J. J . ( 2009).

The strength of anticipatory spatial biasing predicts target discrimination at attended locations: A high-density EEG study

European Journal of Neuroscience, 30, 2224-2234.

URL     [本文引用: 1]

Kelly S. P., Lalor E. C., Reilly R. B., & Foxe J. J . ( 2006).

Increases in alpha oscillatory power reflect an active retinotopic mechanism for distracter suppression during sustained visuospatial attention

Journal of Neurophysiology, 95( 6), 3844-3851.

URL     [本文引用: 1]

Keren A. S., Yuval-Greenberg S., & Deouell L. Y . ( 2010).

Saccadic spike potentials in gamma-band EEG: Characterization, detection and suppression

NeuroImage, 49( 3), 2248-2263.

URL     PMID:19874901      [本文引用: 1]

Analysis of high-frequency (gamma-band) neural activity by means of non-invasive EEG is gaining increasing interest. However, we have recently shown that a saccade-related spike potential (SP) seriously confounds the analysis of EEG induced gamma-band responses (iGBR), as the SP eludes traditional EEG artifact rejection methods. Here we provide a comprehensive profile of the SP and evaluate methods for its detection and suppression, aiming to unveil true cerebral gamma-band activity. The SP appears consistently as a sharp biphasic deflection of about 2202ms starting at the saccade onset, with a frequency band of 6520–9002Hz. On the average, larger saccades elicit higher SP amplitudes. The SP amplitude gradually changes from the extra-ocular channels towards posterior sites with the steepest gradients around the eyes, indicating its ocular source. Although the amplitude and the sign of the SP depend on the choice of reference channel, the potential gradients remain the same and non-zero for all references. The scalp topography is modulated almost exclusively by the direction of saccades, with steeper gradients ipsilateral to the saccade target. We discuss how the above characteristics impede attempts to remove these SPs from the EEG by common temporal filtering, choice of different references, or rejection of contaminated trials. We examine the extent to which SPs can be reliably detected without an eye tracker, assess the degree to which scalp current density derivation attenuates the effect of the SP, and propose a tailored ICA procedure for minimizing the effect of the SP.

Kharate G. K., Patil V. H., & Bhale N. L . ( 2007).

Selection of mother wavelet for image compression on basis of nature of image

Journal of Multimedia, 2( 6), 44-51.

[本文引用: 1]

Kleen J. K., Testorf M. E., Roberts D. W., Scott R. C., Jobst B. J., Holmes G. L., & Lenck-Santini P.-P . ( 2016).

Oscillation phase locking and late ERP components of intracranial hippocampal recordings correlate to patient performance in a working memory task

Frontiers in Human Neuroscience, 10, 287.

[本文引用: 1]

Klimesch, W . ( 1999).

EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis

Brain Research Reviews, 29( 2-3), 169-195.

URL     [本文引用: 1]

Klimesch, W . ( 2012).

alpha-band oscillations, attention, and controlled access to stored information

Trends in Cognitive Sciences, 16( 12), 606-617.

URL     PMID:23141428      [本文引用: 1]

Alpha-band oscillations are the dominant oscillations in the human brain and recent evidence suggests that they have an inhibitory function. Nonetheless, there is little doubt that alpha-band oscillations also play an active role in information processing. In this article, I suggest that alpha-band oscillations have two roles (inhibition and timing) that are closely linked to two fundamental functions of attention (suppression and selection), which enable controlled knowledge access and semantic orientation (the ability to be consciously oriented in time, space, and context). As such, alpha-band oscillations reflect one of the most basic cognitive processes and can also be shown to play a key role in the coalescence of brain activity in different frequencies.

Klimesch W., Schack B., Schabus M., Doppelmayr M., Gruber W., & Sauseng P . ( 2004).

Phase-locked alpha and theta oscillations generate the P1-N1 complex and are related to memory performance

Cognitive Brain Research, 19( 3), 302-316.

URL     [本文引用: 1]

Knakker B., Weiss B., & Vidnyánszky Z . ( 2015).

Object- based attentional selection modulates anticipatory alpha oscillations

Frontiers in Human Neuroscience, 8, 1048.

URL     PMID:4290602      [本文引用: 1]

Abstract Visual cortical alpha oscillations are involved in attentional gating of incoming visual information. It has been shown that spatial and feature-based attentional selection result in increased alpha oscillations over the cortical regions representing sensory input originating from the unattended visual field and task-irrelevant visual features, respectively. However, whether attentional gating in the case of object based selection is also associated with alpha oscillations has not been investigated before. Here we measured anticipatory electroencephalography (EEG) alpha oscillations while participants were cued to attend to foveal face or word stimuli, the processing of which is known to have right and left hemispheric lateralization, respectively. The results revealed that in the case of simultaneously displayed, overlapping face and word stimuli, attending to the words led to increased power of parieto-occipital alpha oscillations over the right hemisphere as compared to when faces were attended. This object category-specific modulation of the hemispheric lateralization of anticipatory alpha oscillations was maintained during sustained attentional selection of sequentially presented face and word stimuli. These results imply that in the case of object-based attentional selection-similarly to spatial and feature-based attention-gating of visual information processing might involve visual cortical alpha oscillations.

Kwon G., Kim M.-Y., Lim S., Kwon H., Lee Y.-H., Kim K., .. Suh M . ( 2015).

Frontoparietal EEG alpha-phase synchrony reflects differential attentional demands during word recall and oculomotor dual-tasks

NeuroReport, 26( 18), 1161-1167.

URL     [本文引用: 1]

Lachaux J. P., Rodriguez E., Martinerie J., & Varela F. J . ( 1999).

Measuring phase synchrony in brain signals

Human Brain Mapping, 8( 4), 194-208.

URL     PMID:10619414      [本文引用: 2]

Abstract <p>This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution (<100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large-scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long-scale effects do reflect cognitive processing, short-scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony. Hum Brain Mapping 8:194鈥208, 1999. 漏 1999 Wiley-Liss, Inc.</p>

Lee, D. T. L., & Yamamoto, A . ( 1994).

Wavelet analysis: Theory and applications

Hewlett-Packard Journal, 45, 44-54.

URL     [本文引用: 1]

Abstract Wavelet analysis has attracted attention for its ability to analyze rapidly changing transient signals. Any application using the Fourier transform can be formulated using wavelets to provide more accurately localized temporal and frequency information. This paper gives an overview of wavelet analysis and describes a software toolbox created by HP Laboratories Japan to aid in the development of wavelet applications.

Lieuw, I . ( 2015).

Time frequency analysis of neural oscillations in multi-attribute decision-making

Scripps senior theses, Paper 556.

URL     [本文引用: 1]

In our daily lives, we often make decisions that require the use of self-control, weighing trade-offs between various attributes: for example, selecting a food based on its health rather than its taste. Previous research suggests that re-weighting attributes may rely on selective attention, associated with decreased neural oscillations over posterior brain regions in the alpha (8-12 Hz) frequency range. Here, we utilized the high temporal resolution and whole-brain coverage of electroencephalography (EEG) to test this hypothesis in data collected from hungry human subjects exercising dietary self-control. Prior analysis of this data has found time-locked neural activity associated with each food鈥檚 perceived taste and health properties from approximately 400 to 650 ms after stimulus onset (Harris et al., 2013). We conducted time-frequency analyses to examine the role of alpha-band oscillations in this attribute weighting. Specifically, we predicted that there would be decreased alpha power in posterior electrodes beginning approximately 400 ms after stimulus onset for the presentation of healthy food relative to unhealthy food, reflecting shifts in selective attention. Consistent with this hypothesis, we found a significant decrease in alpha power for presentations of healthy relative to unhealthy foods. As predicted, this effect was most pronounced at posterior occipital and parietal electrodes and was significant from approximately 450 to 700 ms post-stimulus onset. Additionally, we found significant alpha-band decreases in right temporal electrodes during these times. These results extend previous attention research to multi-attribute choice, suggesting that the re-weighting of attributes can be measured neuro-computationally.

Luck S. J. ( 2005). An introduction to the event-related potential technique. Cambridge: MIT Press.

[本文引用: 1]

Makeig S., Westerfield M., Jung T. P., Enghoff S., Townsend J., Courchesne E., & Sejnowski T. J . ( 2002).

Dynamic brain sources of visual evoked responses

Science, 295( 5555), 690-694.

URL     PMID:11809976      [本文引用: 1]

It has been long debated whether averaged electrical responses recorded from the scalp result from stimulus-evoked brain events or stimulus-induced changes in ongoing brain dynamics. In a human visual selective attention task, we show that nontarget event-related potentials were mainly generated by partial stimulus-induced phase resetting of multiple electroencephalographic processes. Independent component analysis applied to the single-trial data identified at least eight classes of contributing components, including those producing central and lateral posterior alpha, left and right mu, and frontal midline theta rhythms. Scalp topographies of these components were consistent with their generation in compact cortical domains.

Makin A. D. J., Ackerley R., Wild K., Poliakoff E., Gowen E., & El-Deredy W . ( 2011).

Coherent illusory contours reduce microsaccade frequency

Neuropsychologia, 49( 9), 2798-2801.

URL     PMID:21683722      [本文引用: 1]

Synchronized high-frequency gamma band oscillations (30-100 Hz) are thought to mediate the binding of single visual features into whole-object representations. For example, induced gamma band oscillations (iGBRs) have been recorded 280 ms after the onset of a coherent Kanizsa triangle, but not after an incoherent equivalent shape. However, several recent studies have provided evidence that the EEG-recorded iGBR may be a by-product of small saccadic eye movements (microsaccades). Considering these two previous findings, one would hypothesis that there should be more microsaccades following the onset of a coherent Kanizsa triangle. However, we found that microsaccade rebound rate was significantly higher after an incoherent triangle was presented. This result suggests that microsaccades are not a reliable indicator of perceptual binding, and, more importantly, implies that iGBR cannot be universally produced by ocular artefacts.

Mäkinen V., Tiitinen H., & May P . ( 2005).

Auditory event-related responses are generated independently of ongoing brain activity

NeuroImage, 24( 4), 961-968.

URL     PMID:15670673      [本文引用: 1]

Abstract For researchers and clinical practitioners alike, evoked and event-related responses measured with MEG and EEG provide the means for studying human brain function and dysfunction. However, the generation mechanism of event-related responses remains unclear, hindering our ability to formulate viable theories of neural information processing. Event-related responses are assumed to be generated either (1) separately of ongoing, oscillatory brain activity or (2) through stimulus-induced reorganization of ongoing activity. Here, we approached this issue through examining single-trial auditory MEG data in humans. We demonstrate that phase coherence over trials observed with commonly used signal decomposition methods (e.g., wavelets) can result from both a phase-coherent state of ongoing oscillations and from the presence of a phase-coherent event-related response which is additive to ongoing oscillations. To avoid this problem, we introduce a method based on amplitude variance to establish the relationship between ongoing oscillations and event-related responses. We found that auditory stimuli do not give rise to phase reorganization of ongoing activity. Further, increases in spectral power accompany the emergence of event-related responses, and the relationship between spectral power and the amplitude of these responses can be accounted for by a linear summation of the event-related response and ongoing oscillation with a stochastically distributed phase. Thus, on the basis of our observations, auditory event-related responses are unique descriptors of neural information processing in humans, generated by processes separate from and additive to ongoing brain activity.

Marshall T. R., O'Shea J., Jensen O., & Bergmann T. O . ( 2015).

Frontal eye fields control attentional modulation of alpha and gamma oscillations in contralateral occipitoparietal cortex

Journal of Neuroscience, 35, 1638-1647.

URL     [本文引用: 1]

Martinovic J., Gruber T., & Müller M. M . ( 2007).

Induced gamma band responses predict recognition delays during object identification

Journal of Cognitive Neuroscience, 19( 6), 921-934.

URL     PMID:17536963      [本文引用: 1]

Neural mechanisms of object recognition seem to rely on activity of distributed neural assemblies coordinated by synchronous firing in the gamma-band range (>20 Hz). In the present electroencephalogram (EEG) study, we investigated induced gamma band activity during the naming of line drawings of upright objects and objects rotated in the image plane. Such plane-rotation paradigms elicit view-dependent processing, leading to delays in recognition of disoriented objects. Our behavioral results showed reaction time delays for rotated, as opposed to upright, images. These delays were accompanied by delays in the peak latency of induced gamma band responses (GBRs), in the absence of any effects on other measures of EEG activity. The latency of the induced GBRs has thus, for the first time, been selectively modulated by an experimental manipulation that delayed recognition. This finding indicates that induced GBRs have a genuine role as neural markers of late representational processes during object recognition. In concordance with the view that object recognition is achieved through dynamic learning processes, we propose that induced gamma band activity could be one of the possible cortical markers of such dynamic object coding.

Mathewson K. E., Gratton G., Fabiani M., Beck D. M., & Ro T . ( 2009).

To see or not to see: Prestimulus α phase predicts visual awareness

Journal of Neuroscience, 29( 9), 2725-2732.

URL     PMID:19261866      [本文引用: 1]

We often fail to see something that at other times is readily detectable. Because the visual stimulus itself is unchanged, this variability in conscious awareness is likely related to changes in the brain. Here we show that the phase of EEG α rhythm measured over posterior brain regions can reliably predict both subsequent visual detection and stimulus-elicited cortical activation levels in a metacontrast masking paradigm. When a visual target presentation coincides with the trough of an α wave, cortical activation is suppressed as early as 100 ms after stimulus onset, and observers are less likely to detect the target. Thus, during one α cycle lasting 100 ms, the human brain goes through a rapid oscillation in excitability, which directly influences the probability that an environmental stimulus will reach conscious awareness. Moreover, ERPs to the appearance of a fixation cross before the target predict its detection, further suggesting that cortical excitability level may mediate target detection. A novel theory of cortical inhibition is proposed in which increased α power represents a “pulsed inhibition” of cortical activity that affects visual awareness.

Mazaheri, A., & Jensen, O . ( 2006).

Posterior α activity is not phase-reset by visual stimuli

Proceedings of the National Academy of Sciences of the United States of America, 103( 8), 2948-2952.

URL     [本文引用: 1]

Mazaheri, A., & Picton, T. W . ( 2005).

EEG spectral dynamics during discrimination of auditory and visual targets

Cognitive Brain Research, 24( 1), 81-96.

URL     PMID:15922161      [本文引用: 2]

This study measured the changes in the spectrum of the EEG (electroencephalogram) and in the event-related potentials (ERPs) as subjects detected an improbable target in a train of standard stimuli. The intent was to determine how these measurements are related, and to what extent the ERPs might represent phase-locked changes in EEG rhythms. The experimental manipulations were the stimulus modality (auditory or visual), the discriminability of the target, and the presence or absence of distraction. The ERPs showed sensory-evoked potentials that were specific to the modality and a target-evoked P300 wave that was later in the visual modality than in the auditory, and later and smaller when the discrimination was more difficult. The averaged EEG spectrograms showed that targets increased the frontal theta activity, decreased posterior and central alpha and beta activity, and decreased the central gamma activity. The scalp topography of the changes in the alpha and beta activity indicated a posterior desynchronization specific for the visual task and occurring with both targets and standards and a more widespread desynchronization for targets in either modality. Increased phase synchronization occurred during the event-related potentials, but modeling demonstrated that this can be seen when an evoked potential waveform is simply added to the background EEG. However, subtracting the spectrogram of the average ERP from the average spectrogram of the single trials indicated that phase-resetting of the background EEG rhythms can occur during the ERP. The idea that the ERPs and the EEG rhythms hare generators can explain these findings.

Mishra J., Martínez A., Schroeder C. E., & Hillyard S. A . ( 2012).

Spatial attention boosts short-latency neural responses in human visual cortex

NeuroImage, 59( 2), 1968-1978.

URL     PMID:21983181      [本文引用: 4]

In a previous study of visual-spatial attention, Martinez et al. (2007) replicated the well-known finding that stimuli at attended locations elicit enlarged early components in the averaged event-related potential (ERP), which were localized to extrastriate visual cortex. The mechanisms that underlie these attention-related ERP modulations in the latency range of 80-200 ms, however, remain unclear. The main question is whether attention produces increased ERP amplitudes in time-domain averages by augmenting stimulus-triggered neural activity, or alternatively, by increasing the phase-locking of ongoing EEG oscillations to the attended stimuli. We compared these alternative mechanisms using Morlet wavelet decompositions of event-related EEG changes. By analyzing single-trial spectral amplitudes in the theta (4-8 Hz) and alpha (8-12 Hz) bands, which were the dominant frequencies of the early ERP components, it was found that stimuli at attended locations elicited enhanced neural responses in the theta band in the P1 (88-120 ms) and N1 (148-184 ms) latency ranges that were additive with the ongoing EEG. In the alpha band there was evidence for both increased additive neural activity and increased phase-synchronization of the EEG following attended stimuli, but systematic correlations between pre- and post-stimulus alpha activity were more consistent with an additive mechanism. These findings provide the strongest evidence to date in humans that short-latency neural activity elicited by stimuli within the spotlight of spatial attention is boosted or amplified at early stages of processing in extrastriate visual cortex.

Morgan H. M., Muthukumaraswamy S. D., Hibbs C. S., Shapiro K. L., Bracewell R. M., Singh K. D., & Linden, D. E. J . ( 2011).

Feature integration in visual working memory: Parietal gamma activity is related to cognitive coordination

Journal of Neurophysiology, 106( 6), 3185-3194.

URL     [本文引用: 1]

Muthukumaraswamy, S. D . ( 2013).

High-frequency brain activity and muscle artifacts in MEG/EEG: A review and recommendations

Frontiers in Human Neuroscience, 7, 138.

[本文引用: 1]

Neural oscillation . ( 2018, January 5). In Wikipedia, the free encyclopedia. Retrieved January 24, 2018, from

URL     [本文引用: 1]

Ngui W. K., Leong M. S., Hee L. M., & Abdelrhman A. M . ( 2013).

Wavelet analysis: Mother wavelet selection methods

Applied Mechanics and Materials, 393, 953-958.

URL     [本文引用: 1]

Wavelet analysis, being a popular time-frequency analysis method has been applied in various fields to analyze a wide range of signals covering biological signals, vibration signals, acoustic and ultrasonic signals, to name a few. With the capability to provide both time and frequency domains information, wavelet analysis is mainly for time-frequency analysis of signals, signal compression, signal denoising, singularity analysis and features extraction. The main challenge in using wavelet transform is to select the most optimum mother wavelet for the given tasks, as different mother wavelet applied on to the same signal may produces different results. This paper reviews on the mother wavelet selection methods with particular emphasis on the quantitative approaches. A brief description of the proposed new technique to determine the optimum mother wavelet specifically for machinery faults diagnosis is also presented in this paper.

Nobach H., Tropea C., Cordier L., Bonnet J.-P., Delville J., Lewalle J., .. Adrian R . ( 2007).

Review of some fundamentals of data processing

In C. Tropea, A. L. Yarin, & J. F. Foss (Eds.), Springer handbook of experimental fluid mechanics( pp. 1337-1398). Berlin, Heidelberg: Springer.

URL     [本文引用: 2]

This chapter is devoted to reviewing some fundamental transforms and analysis procedures commonly used for both signal and data processing in fluid mechanics measurements. The chapter begins with a brief review of the Fourier transform and its digital counterpart the discrete Fourier transform. In particular its use for estimating power spectral density is discussed in detail. This is followed by an introduction of the correlation function and its relation to the Fourier transform. The Hilbert transform completes the introductory topics. The chapter then turns to a rigorous presentation of the proper orthogonal decomposition (POD) in the context of the approximation theory and as an application of singular value decomposition (SVD). The relationship between POD and SVD is discussed and POD is described in a statistical setting using an averaging operation for use with turbulent flows. The different POD approaches are briefly introduced, whereby the main differences between the classical POD and the snapshot POD are highlighted. This section closes with a presentation of the POD as a generalization of the classical Fourier analysis to inhomogeneous directions. The chapter continues with a discussion of conditional averages and stochastic estimation as a means of studying coherent structures in turbulent flows before moving in a final section to a comprehensive discussion of wavelets as a combination of data processing in time and frequency domain. After first introducing the continuous wavelet transform and orthogonal wavelet transform their application in experimental fluid mechanics is illustrated through numerous examples.

Norcia A. M., Appelbaum L. G., Ales J. M., Cottereau B. R., & Rossion B . ( 2015).

The steady-state visual evoked potential in vision research: A review

Journal of Vision, 15( 6), 4.

URL     PMID:4581566      [本文引用: 1]

Abstract Periodic visual stimulation and analysis of the resulting steady-state visual evoked potentials were first introduced over 80 years ago as a means to study visual sensation and perception. From the first single-channel recording of responses to modulated light to the present use of sophisticated digital displays composed of complex visual stimuli and high-density recording arrays, steady-state methods have been applied in a broad range of scientific and applied settings.The purpose of this article is to describe the fundamental stimulation paradigms for steady-state visual evoked potentials and to illustrate these principles through research findings across a range of applications in vision science.

Palva, S., & Palva, J. M . ( 2011).

Functional roles of alpha-band phase synchronization in local and large-scale cortical networks

Frontiers in Psychology, 2, 204.

[本文引用: 1]

Pampu, N. C . ( 2011).

Study of effects of the short time fourier transform configuration on EEG spectral estimates

Acta Technica Napocensis: Electronics and Telecommunications, 52, 26-29.

URL     [本文引用: 1]

EEG signals recorded from scalp contain useful information about the activity of a large number of neurons. Signal processing is needed to extract this information from the EEG signal. Here we study the effects of configurations of Short Time Fourier Transform (STFT) to determine how the parameters of STFT affect spectral estimations of the mean and relative power in beta and gamma frequency bands of the EEG signal. A statistic analysis was performed showing the effects of several window types and window lengths. The estimation of power in a specific frequency band is affected by the configuration of the STFT.

Penny W. D., Duzel E., Miller K. J., & Ojemann J. G . ( 2008).

Testing for nested oscillation

Journal of Neuroscience Methods, 174( 1), 50-61.

URL     [本文引用: 3]

Pockett S., Bold G. E. J., & Freeman W. J . ( 2009).

EEG synchrony during a perceptual-cognitive task: Widespread phase synchrony at all frequencies

Clinical Neurophysiology, 120( 4), 695-708.

URL     PMID:19250863      [本文引用: 1]

If long-range phase synchrony really is a hallmark of consciousness, it should be present most of the time the subject is conscious. Our results confirm this prediction, and suggest that consciousness may involve not only gamma frequencies, but the whole range from theta to epsilon. The mechanism of synchrony establishment at the scalp as shown by the present method is relatively slow and thus more likely to involve chemical synapses than gap junctions, electric fields or quantum non-locality.

Regan, D . ( 1966).

Some characteristics of average steady-state and transient responses evoked by modulated light

Electroencephalography and Clinical Neurophysiology, 20( 3), 238-248.

URL     PMID:4160391      [本文引用: 1]

Un appareil a été mis au point pour étudier la réponse occipitale en phase à une fréquence de stimulation évoquée par une lamière modulée. L'intensité et la phase sont mesurées sur toute une gamme de fréquences à modulation sinuso07dales, pour des stimuli stables. Une réponse transitoire et une réponse stable moyenne ont été trouvées et principalement identifiées à la région rétinienne centrale. La réponse stable moyenne est indépendante de l'activité alpha. La fréquence alpha est indépendante du stimulus. La réponse stable atteint un maximum, et montre un décalage de phase rapide, au voisinage de 10 c/sec. A des fréquences plus élevées de stimulation modulée, la durée moyenne du décalage de phase de la réponse est proportionnelle à la fréquence du stimulus. Une estimation est faite de la durée de propagation de la composante synchrone. L'atténuation à haute fréquence est moindre que celle obtenue pour le flicker subjectif, mesurée par De Lange. La nature de la réponse en phase et des modéles théoriques sont discutés.

Rihs T. A., Michel C. M., & Thut G . ( 2007).

Mechanisms of selective inhibition in visual spatial attention are indexed by α-band EEG synchronization

European Journal of Neuroscience, 25( 2), 603-610.

URL     [本文引用: 1]

Roach, B. J., & Mathalon, D. H . ( 2008).

Event-related EEG time-frequency analysis: An overview of measures and an analysis of early gamma band phase locking in schizophrenia

Schizophrenia Bulletin, 34( 5), 907-926.

URL     [本文引用: 1]

Rose M., Sommer T., & Büchel C . ( 2006).

Integration of local features to a global percept by neural coupling

Cerebral Cortex, 16( 10), 1522-1528.

URL     PMID:16339083      [本文引用: 1]

Abstract The integration of different visual attributes into the percept of a single global shape is a central aspect of object processing. In hierarchically organized stimuli with local and global levels, the attentional focus largely determines which level is processed. Here we tested the hypothesis that object processing during attention to the global aspect of the stimulus is characterized by an increased neural coupling between visual areas reflecting the integration of local features. In the present experiment, we used global letters that were constructed by smaller local letters, and a cue signaled which spatial level should be identified. On the local level, only 1 relevant letter was presented laterally in 1 visual hemifield. In contrast, the global letter extended into both hemifields, and the integration of information from both hemispheres was necessary to identify the global stimulus. Therefore, we expected an increased functional coupling between hemispheres during global processing. This hypothesis was investigated using electroencephalographic recordings and an analysis of phase locking and coherence. The results show that stimulus-locked neural coupling within the gamma band (30-40 Hz) across hemispheres in visual cortex increased for global processing after stimulus presentation and could therefore reflect the integration of local visual information.

Rossion B., Prieto E. A., Boremanse A., Kuefner D., & van Belle G . ( 2012).

A steady-state visual evoked potential approach to individual face perception: Effect of inversion, contrast-reversal and temporal dynamics

NeuroImage, 63( 3), 1585-1600.

URL     [本文引用: 1]

Samiee, S., & Baillet, S . ( 2017).

Time-resolved phase- amplitude coupling in neural oscillations

NeuroImage, 159, 270-279.

URL     PMID:28757194      [本文引用: 1]

react-text: 433 The electrophysiological study of cochlear microphonics (CM), whole nerve action potential (AP) and endocochlear potential (EP) were examined. (1) With the extension of the exposure time of 500 Hz tone, a decrease of CM maximum output voltage in test frequency from 2 to 6 kHz was observed. (2) N1 potential of AP decreased very significantly by 500 Hz tone exposure. (3) A very pronounced... /react-text react-text: 434 /react-text [Show full abstract]

Sauseng P., Klimesch W., Stadler W., Schabus M., Doppelmayr M., Hanslmayr S., .. Birbaumer N . ( 2005).

A shift of visual spatial attention is selectively associated with human EEG alpha activity

European Journal of Neuroscience, 22( 11), 2917-2926.

URL     PMID:16324126      [本文引用: 1]

Abstract Event-related potentials and ongoing oscillatory electroencephalogram (EEG) activity were measured while subjects performed a cued visual spatial attention task. They were instructed to shift their attention to either the left or right visual hemifield according to a cue, which could be valid or invalid. Thereafter, a peripheral target had to be evaluated. At posterior parietal brain areas early components of the event-related potential (P1 and N1) were higher when the cue had been valid compared with invalid. An anticipatory attention effect was found in EEG alpha magnitude at parieto-occipital electrode sites. Starting 200ms before target onset alpha amplitudes were significantly stronger suppressed at sites contralateral to the attended visual hemifield than ipsilateral to it. In addition, phase coupling between prefrontal and posterior parietal electrode sites was calculated. It was found that prefrontal cortex shows stronger phase coupling with posterior sites that are contralateral to the attended hemifield than ipsilateral sites. The results suggest that a shift of attention selectively modulates excitability of the contralateral posterior parietal cortex and that this posterior modulation of alpha activity is controlled by prefrontal regions.

Selesnick, I. W . ( 2011).

Wavelet transform with tunable Q-factor

IEEE Transactions on Signal Processing, 59( 8), 3560-3575.

URL     [本文引用: 1]

This paper describes a discrete-time wavelet transform for which the Q-factor is easily specified. Hence, the transform can be tuned according to the oscillatory behavior of the signal to which it is applied. The transform is based on a real-valued scaling factor (dilation-factor) and is implemented using a perfect reconstruction over-sampled filter bank with real-valued sampling factors. Two forms of the transform are presented. The first form is defined for discrete-time signals defined on all of Z. The second form is defined for discrete-time signals of finite-length and can be implemented efficiently with FFTs. The transform is parameterized by its Q-factor and its oversampling rate (redundancy), with modest oversampling rates (e.g., three to four times overcomplete) being sufficient for the analysis/synthesis functions to be well localized.

Sharma, A., & Singh, M . ( 2015).

Assessing alpha activity in attention and relaxed state: An EEG analysis

In 2015 1st international conference on next generation computing technologies ( pp. 508-513). Dehradun: IEEE.

URL     [本文引用: 1]

An electroencephalogram is a recording of brains spontaneous electrical activity. This is controlled by billions of neurons. These neurons continually send messages to each other which can be picked up as electrical impulses from the scalp. The process of picking up and recording the impulses is known as EEG. An EEG can be divided into four basic frequency bands namely delta, theta, alpha and beta. This paper shows changes in the power of alpha frequency band on performing attention tasks. It is observed that mean alpha power decreased while performing attention task as compared to mean alpha power in relaxed state in the frontal and occipital region of the brain. No statistically significant changes in alpha power are found in prefrontal region of the brain during attention tasks.

Siegel M., Donner T. H., Oostenveld R., Fries P., & Engel A. K . ( 2008).

Neuronal synchronization along the dorsal visual pathway reflects the focus of spatial attention

Neuron, 60( 4), 709-719.

URL     [本文引用: 3]

Snyder, A. C., & Foxe, J. J . ( 2010).

Anticipatory attentional suppression of visual features indexed by oscillatory alpha-band power increases: A high-density electrical mapping study

Journal of Neuroscience, 30( 11), 4024-4032.

URL     [本文引用: 1]

Song K., Meng M., Chen L., Zhou K., & Luo H . ( 2014).

Behavioral oscillations in attention: Rhythmic α pulses mediated through θ band

Journal of Neuroscience, 34( 14), 4837-4844.

URL     PMID:24695703      [本文引用: 5]

Neuronal oscillations are ubiquitous in the brain and contribute to perception and attention. However, most associated evidence derives from post hoc correlations between brain dynamics and behavior. Although a few recent studies demonstrate rhythms in behavior, it remains largely unknown whether behavioral performances manifest spectrotemporal dynamics in a neurophysiologically relevant manner (e. g., the temporal modulation of ongoing oscillations, the cross-frequency coupling). To investigate the issue, we examined fine spectrotemporal dynamics of behavioral time courses in a large sample of human participants (n=49), by taking a high time-resolved psychophysical measurement in a precuing attentional task. We observed compelling dynamic oscillatory patterns directly in behavior. First, typical attentional effects are demonstrated in low-pass (0-2 Hz) filtered time courses of behavioral responses. Second, an uninformative peripheral cue elicits recurring alpha-band (8-20 Hz) pulses in behavioral performances, and the elicited alpha pulses for cued and uncued conditions are in a temporally alternating relationship. Finally, ongoing alpha-band power is phase locked to ongoing theta-bands (3-5 Hz) in behavioral time courses. Our findings constitute manifestation of oscillations at physiologically relevant rhythms and power-phase locking, as widely observed in neurophysiological recordings, in behavior. The findings suggest that behavioral performance actually consists of rich dynamic information and may reflect underlying neuronal oscillatory substrates. Our data also speak to a neural mechanism for item attention based on successive cycles (theta) of a sequential attentional sampling (alpha) process.

Spencer K. M., Nestor P. G., Niznikiewicz M. A., Salisbury D. F., Shenton M. E., & McCarley R. W . ( 2003).

Abnormal neural synchrony in schizophrenia

Journal of Neuroscience, 23( 19), 7407-7411.

URL     [本文引用: 1]

StÖrmer V., Feng W. F., Martinez A., McDonald J. J., & Hillyard S. A . ( 2016).

Salient, irrelevant sounds reflexively induce alpha rhythm desynchronization in parallel with slow potential shifts in visual cortex

Journal of Cognitive Neuroscience, 28( 3), 433-445.

URL     [本文引用: 1]

Tallon-Baudry, C . ( 2009).

The roles of gamma-band oscillatory synchrony in human visual cognition

Frontiers in Bioscience, 14, 321-332.

URL     PMID:19273069      [本文引用: 1]

Abstract Oscillatory synchrony in the gamma (30-120 Hz) range has initially been related both theoretically and experimentally to visual grouping. Its functional role in human visual cognition turns out to be much broader, especially when attention, memory or awareness are concerned. Induced gamma oscillations are thus not related to a single cognitive function, and are probably better understood in terms of a population mechanism taking advantage of the neuron's fine temporal tuning: the 10-30 ms time precision imposed by gamma-band rhythms could favor the selective transmission of synchronized information (attention) and foster synaptic plasticity (memory). Besides, gamma oscillatory synchrony also seems related to the emergence of visual awareness. The recent discovery that gamma oscillations could appear simultaneously in distinct areas at distinct frequencies and with different functional correlates further suggests the existence of a flexible multiplexing schema, integrating frequency bands within the gamma range but also at lower frequency bands. Understanding how and when oscillations at different frequencies interact has become a major challenge for the years to come.

Tallon-Baudry, C., & Bertrand, O . ( 1999).

Oscillatory gamma activity in humans and its role in object representation

Trends in Cognitive Sciences, 3( 4), 151-162.

URL     PMID:10322469      [本文引用: 3]

Abstract We experience objects as whole, complete entities irrespective of whether they are perceived by our sensory systems or are recalled from memory. However, it is also known that many of the properties of objects are encoded and processed in different areas of the brain. How then, do coherent representations emerge? One theory suggests that rhythmic synchronization of neural discharges in the gamma band (around 40 Hz) may provide the necessary spatial and temporal links that bind together the processing in different brain areas to build a coherent percept. In this article we propose that this mechanism could also be used more generally for the construction of object representations that are driven by sensory input or internal, top-down processes. The review will focus on the literature on gamma oscillatory activities in humans and will describe the different types of gamma responses and how to analyze them. Converging evidence that suggests that one particular type of gamma activity (induced gamma activity) is observed during the construction of an object representation will be discussed.

Tallon-Baudry C., Bertrand O., & Fischer C . ( 2001).

Oscillatory synchrony between human extrastriate areas during visual short-term memory maintenance

Journal of Neuroscience, 21, RC177.

[本文引用: 1]

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α-band electroencephalographic activity over occipital cortex indexes visuospatial attention bias and predicts visual target detection

Journal of Neuroscience, 26( 37), 9494-9502.

URL     [本文引用: 1]

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Bulletin of the American Meteorological Society, 79( 1), 61-78.

URL     [本文引用: 1]

Tort A. B. L., Komorowski R. W., Manns J. R., Kopell N. J., & Eichenbaum H . ( 2009).

Theta-gamma coupling increases during the learning of item-context associations

Proceedings of the National Academy of Sciences of the United States of America, 106( 49), 20942-20947.

URL     [本文引用: 1]

Phase-amplitude cross-frequency coupling (CFC) between theta (4-12 Hz) and gamma (30-100 Hz) oscillations occurs frequently in the hippocampus. However, it still remains unclear whether theta-gamma coupling has any functional significance. To address this issue, we studied CFC in local field potential oscillations recorded from the CA3 region of the dorsal hippocampus of rats as they learned to associate items with their spatial context. During the course of learning, the amplitude of the low gamma subband (30-60 Hz) became more strongly modulated by theta phase in CA3, and higher levels of theta-gamma modulation were maintained throughout overtraining sessions. Furthermore, the strength of theta-gamma coupling was directly correlated with the increase in performance accuracy during learning sessions. These findings suggest a role for hippocampal theta-gamma coupling in memory recall.

Tseng P., Chang Y. T., Chang C. F., Liang W. K., & Juan C. H . ( 2016).

The critical role of phase difference in gamma oscillation within the temporoparietal network for binding visual working memory

Scientific Reports, 6, 32138.

URL     [本文引用: 1]

Uusberg A., Uibo H., Kreegipuu K., & Allik J . ( 2013).

EEG alpha and cortical inhibition in affective attention

International Journal of Psychophysiology, 89( 1), 26-36.

URL     [本文引用: 2]

Recent progress in cognitive neuroscience suggests that alpha activity may reflect selective cortical inhibition involved in signal amplification, rather than neural idling. Unfortunately, these theoretical advances remain largely ignored in affective neuroscience. To address this limitation the present paper proposes a novel research avenue aimed at using alpha to elucidate cortical inhibitory mechanisms involved in affective processes. The proposal is illustrated by developing inhibitory accounts of affective attention and affective tuning phenomena. The emergent predictions were tested using event-related perturbations from 73 students evaluating affective and nonaffective aspects of five types of emotional images. The results revealed that upper alpha power was increased by affective content in general and aversive stimuli in particular from 350 ms at posterior and from 575 ms at central sites. The evaluation task interacted with affective content only at a liberal statistical significance level in late posterior alpha. These results are generally in line with the proposed inhibitory accounts of affective attention and tuning, although the evidence is preliminary rather than conclusive. As confirmation of functional origins of alpha in affect remains beyond the scope of a single study, this paper aims to inspire further extrapolation of the inhibitory account of alpha within affective neuroscience.

Valipour S., Shaligram A. D., & Kulkarni G. R . ( 2013).

Spectral analysis of EEG signal for detection of alpha rhythm with open and closed eyes

International Journal of Engineering and Innovative Technology, 3( 6), 1-4.

van Gerven, M., & Jensen, O . ( 2009).

Attention modulations of posterior alpha as a control signal for two-dimensional brain-computer interfaces

Journal of Neuroscience Methods, 179, 78-84.

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Waldert S., Preissl H., Demandt E., Braun C., Birbaumer N., Aertsen A., & Mehring C . ( 2008).

Hand movement direction decoded from MEG and EEG

Journal of Neuroscience, 28( 4), 1000-1008.

URL     PMID:18216207      [本文引用: 1]

Abstract Brain activity can be used as a control signal for brain-machine interfaces (BMIs). A powerful and widely acknowledged BMI approach, so far only applied in invasive recording techniques, uses neuronal signals related to limb movements for equivalent, multidimensional control of an external effector. Here, we investigated whether this approach is also applicable for noninvasive recording techniques. To this end, we recorded whole-head MEG during center-out movements with the hand and found significant power modulation of MEG activity between rest and movement in three frequency bands: an increase for < or = 7 Hz (low-frequency band) and 62-87 Hz (high-gamma band) and a decrease for 10-30 Hz (beta band) during movement. Movement directions could be inferred on a single-trial basis from the low-pass filtered MEG activity as well as from power modulations in the low-frequency band, but not from the beta and high-gamma bands. Using sensors above the motor area, we obtained a surprisingly high decoding accuracy of 67% on average across subjects. Decoding accuracy started to rise significantly above chance level before movement onset. Based on simultaneous MEG and EEG recordings, we show that the inference of movement direction works equally well for both recording techniques. In summary, our results show that neuronal activity associated with different movements of the same effector can be distinguished by means of noninvasive recordings and might, thus, be used to drive a noninvasive BMI.

Womelsdorf T., Johnston K., Vinck M., & Everling S . ( 2010).

Theta-activity in anterior cingulate cortex predicts task rules and their adjustments following errors

Proceedings of the National Academy of Sciences of the United States of America, 107( 11), 5248-5253.

URL     [本文引用: 1]

Woodman, G. F . ( 2010).

A brief introduction to the use of event-related potentials in studies of perception and attention

Attention, Perception, & Psychophysics, 72( 8), 2031-2046.

[本文引用: 1]

Worden M. S., Foxe J. J., Wang N., & Simpson G. V . ( 2000).

Anticipatory biasing of visuospatial attention indexed by retinotopically specific α-band electroencephalography increases over occipital cortex

The Journal of Neuroscience, 20, RC63.

[本文引用: 2]

Yuval-Greenberg S., Tomer O., Keren A. S., Nelken I., & Deouell L. Y . ( 2008).

Transient induced gamma-band response in EEG as a manifestation of miniature saccades

Neuron, 58( 3), 429-441.

URL     PMID:18466752     

Abstract The induced gamma-band EEG response (iGBR) recorded on the scalp is widely assumed to reflect synchronous neural oscillation associated with object representation, attention, memory, and consciousness. The most commonly reported EEG iGBR is a broadband transient increase in power at the gamma range approximately 200-300 ms following stimulus onset. A conspicuous feature of this iGBR is the trial-to-trial poststimulus latency variability, which has been insufficiently addressed. Here, we show, using single-trial analysis of concomitant EEG and eye tracking, that this iGBR is tightly time locked to the onset of involuntary miniature eye movements and reflects a saccadic "spike potential." The time course of the iGBR is related to an increase in the rate of saccades following a period of poststimulus saccadic inhibition. Thus, whereas neuronal gamma-band oscillations were shown conclusively with other methods, the broadband transient iGBR recorded by scalp EEG reflects properties of miniature saccade dynamics rather than neuronal oscillations.

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