Delorme Arnaud, Makeig Scott
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California at San Diego, La Jolla 92093-0961, USA.
IEEE Trans Neural Syst Rehabil Eng. 2003 Jun;11(2):133-7. doi: 10.1109/TNSRE.2003.814428.
We analyzed 15 sessions of 64-channel electroencephalographic (EEG) data recorded from a highly trained subject during sessions in which he attempted to regulate power at 12 Hz over his left- and right-central scalp to control the altitude of a cursor moving toward target boxes placed at the top-, middle-, or bottom-right of a computer screen. We used infomax independent component analysis (ICA) to decompose 64-channel EEG data from trials in which the subject successfully up- or down-regulated the measured EEG signals. Applying time-frequency analysis to the time courses of activity of several of the resulting 64 independent EEG components revealed that successful regulation of the measured activity was accompanied by extensive, asymmetrical changes in power and coherence, at both nearby and distant frequencies, in several parts of cortex. A more complete understanding of these phenomena could help to explain the nature and locus of learned regulation of EEG rhythms and might also suggest ways to further optimize the performance of brain-computer interfaces.
我们分析了从一名训练有素的受试者身上记录的15次64通道脑电图（EEG）数据，这些数据记录于他试图调节左、右中央头皮上12赫兹的功率，以控制光标朝向位于电脑屏幕右上角、中间或右下角的目标框移动的高度的过程中。我们使用信息最大化独立成分分析（ICA）来分解受试者成功上调或下调测量到的EEG信号的试验中的64通道EEG数据。对由此产生的64个独立EEG成分中的几个成分的活动时间进程进行时频分析发现，成功调节测量到的活动伴随着皮质多个部位在附近和远处频率上功率和相干性的广泛、不对称变化。对这些现象的更全面理解有助于解释EEG节律学习调节的性质和位置，也可能为进一步优化脑机接口性能提供方法。
