Independent Component Analysis of Simulated ERP Data
A recently-derived algorithm for performing Independent Component Analysis (ICA) (Bell & Sejnowski, 1995) based on information maximization is a new information-theoretic approach to the problem of separating multichannel electroencephalographic (EEG) or magnetoencephalographic (MEG) data into temporally independent and spatially stationary sources (Makeig et al., 1996). In a previous report, we have shown that the algorithm can separate simulated EEG source waveforms (independent simulated brain source activities mixed linearly at the scalp sensors), even in the presence of multiple low-level model brain and sensor noise sources (Ghahremani et al., 1996). Here, we demonstrate the ability of the ICA algorithm to decompose brief event-related potential (ERP) data sets into temporally independent components (Makeig et al., 1997) by applying it to simulated ERP-length EEG data synthesized from 3-sec (600-point) electrocorticographic (ECoG) epochs recorded from the cortical surface of a human undergoing pre-surgical evaluation (Bullock et al., 1995a, 1995b). Six asynchronous single-channel ECoG data epochs were projected through single-and multiple-dipole model sources in a three-shell spherical head model (Dale & Sereno, 1993) to six simulated scalp sensors to create simulated EEG data. In two sets of simulation experiments, we altered relative source strengths, added multiple low-level sources (synthesized from ECoG data and uniform-or Gaussian-distributed noise), and permuted the simulated dipole source locations and orientations. The algorithm reliably separated the activities of the relatively strong sources, regardless of source location, dipole orientation, and low-level source distributions. Recovery of the original component waveforms was much better using ICA than using PCA without or without Varimax or Promax rotation. Thus, the ICA algorithm should identify relatively strong, temporally independent and spatially overlapping ERP components arising from multiple brain and/or non-brain sources, regardless of their spatial distributions. This shows that the ICA algorithm can decompose ERPs generated by uncorrelated sources. A third ERP simulation tested how the algorithm treated a simulated ERP epoch constructed using model ERP generators whose activations were partially correlated. In this case, the algorithm parsed the simulated ERP waveforms into a sum of temporally independent and spatially stationary components reflecting the changing topography of correlated source activity in the simulated ERP data. Each of the affected components sums activity from one or more concurrently-active brain generators. This suggests the ICA algorithm may also be useful for identifying event-related changes in the correlation structure of either spontaneous or event-related EEG data. Paradoxically, adding four simulated " no response " epochs to the training data minimized …