Date Published: June 15, 2004
Publisher: Public Library of Science
Author(s): Scott Makeig, Arnaud Delorme, Marissa Westerfield, Tzyy-Ping Jung, Jeanne Townsend, Eric Courchesne, Terrence J Sejnowski
Abstract: Scalp-recorded electroencephalographic (EEG) signals produced by partial synchronization of cortical field activity mix locally synchronous electrical activities of many cortical areas. Analysis of event-related EEG signals typically assumes that poststimulus potentials emerge out of a flat baseline. Signals associated with a particular type of cognitive event are then assessed by averaging data from each scalp channel across trials, producing averaged event-related potentials (ERPs). ERP averaging, however, filters out much of the information about cortical dynamics available in the unaveraged data trials. Here, we studied the dynamics of cortical electrical activity while subjects detected and manually responded to visual targets, viewing signals retained in ERP averages not as responses of an otherwise silent system but as resulting from event-related alterations in ongoing EEG processes. We applied infomax independent component analysis to parse the dynamics of the unaveraged 31-channel EEG signals into maximally independent processes, then clustered the resulting processes across subjects by similarities in their scalp maps and activity power spectra, identifying nine classes of EEG processes with distinct spatial distributions and event-related dynamics. Coupled two-cycle postmotor theta bursts followed button presses in frontal midline and somatomotor clusters, while the broad postmotor “P300” positivity summed distinct contributions from several classes of frontal, parietal, and occipital processes. The observed event-related changes in local field activities, within and between cortical areas, may serve to modulate the strength of spike-based communication between cortical areas to update attention, expectancy, memory, and motor preparation during and after target recognition and speeded responding.
Partial Text: The waking brain updates and fulfills intentions through brain processes that operate within and across multiple brain areas to integrate perception, association, and action. Fulfillment of intentions is facilitated by features of these and other processes that support informed anticipation of and selective attention to events and their probable consequences. The dynamics of ongoing electroencephalographic (EEG) activity recorded from the human scalp differ markedly with state of attention and intention (Makeig and Inlow 1993; Worden et al. 2000), yet most event-related EEG research has assumed that the effects of events on EEG signals emerge out of a flat baseline, as in the typical averaged event-related potential (ERP). The electrophysiological consequences of stimulus events spread quickly in the brain. By 50–150 ms, sensory stimulus information is widely distributed (Hupe et al. 2001), perturbing ongoing patterns of local field activity in many brain areas (Klopp et al. 2000). There is little chance, therefore, that any but still earlier ERP features occur within single brain areas.
The independent component analysis (ICA) method provides a complete decomposition of single-trial (or continuous) EEG data, separating the data into distinct information sources. As the results presented below show, the amount of information about cortical dynamics provided by this method is large. Here, we detail for the first time dynamics occurring within single trials of the classes of maximally independent EEG processes whose event-related activities contribute to 31-channel visual target responses recorded during a test of spatial selective visual attention (Figure 1).
ICA used the temporal information contained in the single-trial EEG time courses to identify and separate maximally independent processes. These were associated with overlapping scalp maps and time courses whose distinctive features were no longer blurred by volume conduction as in the scalp electrode data. The nine independent component clusters here identified by their similar scalp projections and activity spectra resemble classes of EEG phenomena long described by neurologists from observation of paper data displays—central and lateral alpha, left and right mu, and FM theta rhythms. By cleanly separating the EEG contributions of these processes, ICA allowed exploration of their individual and joint event-related dynamics. Our finding of selective theta synchronization between FM, motor, and parietal processes (Video S1) was only possible using ICA. The clear separation of “alpha ringing” in the stimulus-locked response from the other ERP features (see Figure 10G) also illustrates the power of ICA to separate temporally and functionally distinct activities that are generated in different brain areas but project to the same scalp channels.