S. Makeig et al., BLIND SEPARATION OF AUDITORY EVENT-RELATED BRAIN RESPONSES INTO INDEPENDENT COMPONENTS, Proceedings of the National Academy of Sciences of the United Statesof America, 94(20), 1997, pp. 10979-10984
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.