A Framework for Evaluating ICA Methods of Artifact Removal from Multichannel EEG

We present a method for evaluating ICA separation of artifacts from EEG (electroencephalographic) data. Two algorithms, Infomax and FastICA, were applied to "synthetic data," created by superimposing simulated blinks on a blink-free EEG. To examine sensitivity to different data characteristics, multiple datasets were constructed by varying properties of the simulated blinks. ICA was used to decompose the data, and each source was cross-correlated with a blink template. Different thresholds for correlation were used to assess stability of the algorithms. When a match between the blink-template and the decomposition was obtained, the contribution of the source was subtracted from the EEG. Since the original data were known a priori to be blink-free, it was possible to compute the correlation between these "baseline" data and the results of different decompositions. By averaging the filtered data, time-locked to the simulated blinks, we illustrate effects of different outcomes for EEG waveform and topographic analysis.