Electrode Selection Based on FDA techniques for EEG Signal Classification

We propose a completely data driven two pass electrode selection technique based on functional data analysis for EEG classification. In the first pass, electrodes are selected based on their activity and in the second pass, selected electrodes are retained based on their discriminant activity. Electrodes are ranked and based on their connectivity, informative clusters are extracted. We test our method on a face familiarity task and demonstrate the efficacy of our channel selection method over all channels and commonly used channels in face familiarity task.