Optimal channel selection for multi-channel ssvep detection and classification in bcis

Many multi-channel techniques for Steady-State Visual-Evoked Potential (SSVEP) detection from EEG have shown significant improvement in the performance of Brain-Computer Interfaces (BCIs). Multichannel methods, generally involve deriving a spatial filter to linearly combine the EEG channels so as to minimize the noise energy and enhance the SSVEP response. In this paper, three state of the art multichannel techniques are studied and compared. The performance of the classifiers for varying number and combination of the EEG channels is studied to determine the optimal choice of channels that yield maximum classification accuracy. The correlation of different channel parameters with the net montage performance is also investigated. Results indicate that Minimum Energy Channel (MEC) based classifier yields the highest accuracy values using 6 channels for all the 3 subjects. Significance of non-occipital locations for signal acquisition has been observed. Further, results indicate that the choice of channels to be used in the montage is to be made keeping in mind their effective signal strength, co-channel noise correlation values and signal to noise ratios. This ensures that a particular montage has effectively assimilated the signal and noise components.