使用主成分分析（PCA）从视觉Oddball任务中分解δ、θ和α时间频率事件相关电位（ERP）活动。
Decomposing delta, theta, and alpha time-frequency ERP activity from a visual oddball task using PCA.
作者信息
Bernat Edward M, Malone Stephen M, Williams William J, Patrick Christopher J, Iacono William G
机构信息
Department of Psychology, University of Minnesota, Elliott Hall, 75 East River Road, Minneapolis, MN 55455, USA.
出版信息
Int J Psychophysiol. 2007 Apr;64(1):62-74. doi: 10.1016/j.ijpsycho.2006.07.015. Epub 2006 Oct 5.
OBJECTIVE
Time-frequency (TF) analysis has become an important tool for assessing electrical and magnetic brain activity from event-related paradigms. In electrical potential data, theta and delta activities have been shown to underlie P300 activity, and alpha has been shown to be inhibited during P300 activity. Measures of delta, theta, and alpha activity are commonly taken from TF surfaces. However, methods for extracting relevant activity do not commonly go beyond taking means of windows on the surface, analogous to measuring activity within a defined P300 window in time-only signal representations. The current objective was to use a data driven method to derive relevant TF components from event-related potential data from a large number of participants in an oddball paradigm.
METHODS
A recently developed PCA approach was employed to extract TF components [Bernat, E. M., Williams, W. J., and Gehring, W. J. (2005). Decomposing ERP time-frequency energy using PCA. Clin Neurophysiol, 116(6), 1314-1334] from an ERP dataset of 2068 17 year olds (979 males). TF activity was taken from both individual trials and condition averages. Activity including frequencies ranging from 0 to 14 Hz and time ranging from stimulus onset to 1312.5 ms were decomposed.
RESULTS
A coordinated set of time-frequency events was apparent across the decompositions. Similar TF components representing earlier theta followed by delta were extracted from both individual trials and averaged data. Alpha activity, as predicted, was apparent only when time-frequency surfaces were generated from trial level data, and was characterized by a reduction during the P300.
CONCLUSIONS
Theta, delta, and alpha activities were extracted with predictable time-courses. Notably, this approach was effective at characterizing data from a single-electrode. Finally, decomposition of TF data generated from individual trials and condition averages produced similar results, but with predictable differences. Specifically, trial level data evidenced more and more varied theta measures, and accounted for less overall variance.
目的
时频（TF）分析已成为从事件相关范式评估脑电和脑磁活动的重要工具。在电位数据中，θ波和δ波活动已被证明是P300活动的基础，而α波在P300活动期间被证明受到抑制。δ波、θ波和α波活动的测量通常取自TF表面。然而，提取相关活动的方法通常不超过对表面上的窗口取平均值，这类似于在仅时间信号表示中测量定义的P300窗口内的活动。当前的目标是使用数据驱动方法从大量参与者在oddball范式中的事件相关电位数据中导出相关的TF成分。
方法
采用一种最近开发的主成分分析（PCA）方法从2068名17岁青少年（979名男性）的ERP数据集中提取TF成分[伯纳特，E.M.，威廉姆斯，W.J.，和格林，W.J.（2005年）。使用PCA分解ERP时频能量。临床神经生理学，116(6)，1314 - 1334]。TF活动取自单个试验和条件平均值。分解的活动包括频率范围从0到14赫兹以及时间范围从刺激开始到1312.5毫秒。
结果
在整个分解过程中，一组协调的时频事件是明显的。从单个试验和平均数据中都提取出了类似的TF成分，先是早期的θ波，随后是δ波。正如预期的那样，α波活动仅在从试验水平数据生成时频表面时才明显，其特征是在P300期间减少。
结论
θ波、δ波和α波活动是以可预测的时间进程提取的。值得注意的是，这种方法在表征单电极数据方面是有效的。最后，从单个试验和条件平均值生成的TF数据的分解产生了相似的结果，但存在可预测的差异。具体而言，试验水平数据显示出越来越多样的θ波测量值，并且占总体方差的比例较小。
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