BLIND SEPARATION OF AUDITORY EVENT-RELATED BRAIN RESPONSES INTO INDEPENDENT COMPONENTS

Averaged event-related potential (ERR) data recorded from the human sc alp reveal electroencephalographic (EEG) activity that is reliably tim e-locked and phase-locked to experimental events, We report here the a pplication of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of compone nts with fixed scalp distributions and sparsely activated, maximally i ndependent time courses, Independent component analysis (ICA) decompos es ERP data into a number of components equal to the number of sensors , The derived components have distinct but not necessarily orthogonal scalp projections, Unlike dipole-fitting methods, the algorithm does n ot model the locations of their generators in the head, Unlike methods that remove second-order correlations, such as principal component an alysis (PCA), ICA also minimizes higher-order dependencies, Applied to detected-and undetected-target ERPs from an auditory vigilance experi ment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions, Three of these components were active only when the subject detected the targets, three other components only wh en the target went undetected, and one in both cases, Three additional components accounted for the steady-state brain response to a 39-Hz b ackground click train, Major features of the decomposition proved robu st across sessions and changes in sensor number and placement, This me thod of ERP analysis can be used to compare responses from multiple st imuli, task conditions, and subject states.