Society for Neuroscience
San Diego, CA
Oct. 23-27
R.-S. HUANG, T.-P. JUNG* and S. MAKEIG* SCCN / INC , UCSD, San Diego, CA, 92093 , Institute for Neural Computation, University of California San Diego
Analyzing brain dynamics in a continuous compensatory tracking task
The dynamics of electroencephalographic (EEG) activity in a continuous visuospatial compensatory tracking task were analyzed by independent component analysis (ICA) and time-frequency techniques. In one-hour sessions, high-density EEG (70+2 leads) was recorded while healthy volunteers attempted to use a trackball to keep a drifting disc in a bulls-eye in the center of screen. Disc trajectory was converted into a time series measure of disc error. Local minima indicated moments when the disc started to drift away from the center. Subject performance was indexed by root mean square disc error in a 20s epoch centered on each local minimum, high error generally indicating drowsiness. Maximally independent EEG processes and their dipole source locations were obtained using the EEGLAB toolbox (sccn.ucsd.edu/eeglab). Component activation were epoched in 5s time intervals, 2s preceding and 3s following each local minimum. In the first subject tested, significant spectral perturbations were observed in high-error epochs when the disc began to escape from the center. Four of 70 components were located in or near primary visual cortex. Of them three components showed increase in tonic alpha/theta activities when the subject was drowsy and phasic alpha/theta activities when the disc escaped from the center, while the other component showed tonic 10 and 20Hz and phasic 20Hz spectral perturbations. In addition, theta activity in or near anterior cingulate gyrus also increased when the disk escaped from the center. EEG activity between 10 and 30Hz was larger in the left somatomotor cortex when the disc was moving from local minimum to local maximum, and alpha activity persisted after the local maximum. The time-frequency patterns of these components were consistently observed across three sessions from this subject. Thus, changes of EEG brain dynamics can be modeled in a continuous behavioral task without discrete event onsets.