Foad Ghaderi, Elsa Andrea Kirchner
Electroencephalogram, eye artifacts, independent compo-nent analysis, artifact removal.
Eye artifacts, i.e., blinks and saccades, are usually non- avoidable when recording electroencephalogram (EEG) data. These artifacts can affect the performance of classify- ing the EEG patterns especially in real world applications, e.g. brain computer interfaces. To evaluate the effective- ness of independent component analysis (ICA) based eye artifact removal methods, the data are analyzed in batch and window-based modes in this paper. Despite the im- provements achieved in the batch mode, it turns out that applying the removal methods to overlapping windows of the EEG data stream does not improve the classification performance.
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