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Improving Classification Accuracy in Cortical Surface Recordings Using ICA-Based Features

Performance of classifiers on electrophysiological signals are often affected by volume conduction, thus compromising their reliability and classification accuracy. This issue is usually incorrectly overlooked when dealing with electrocorticography (ECoG) recordings. Here we propose that preprocessing ECoG signals using Independent Component Analysis (ICA) can improve classification performance. To test this hypothesis we use ECoG signals measured from the cortical surface of an epileptic subject. ECoG signals from subtemporal cortex were recorded while a series of face and house images were displayed briefly. We compare the performance of house versus face classifiers using features extracted from the recorded signals versus their independent components (ICs). We show that classification accuracy based on IC features is preserved when the channels with the highest single channel classification accuracy are removed from the analysis. Hence, features of independent signal spaces derived by ICA decomposition may improve the robustness and reliability of signal-based Brain-Computer Interface (BCI) classifiers.