Improving Separability of Eeg Signals During Motor Imagery With An Efficient Circular Laplacian

This paper reports on a new EEG re-referencing scheme, known as the circular Laplacian, for processing multichannel EEG signals. The new reference signals are derived from the average potentials on the circles around the electrodes. The radii of the circles can be adjusted to achieve spatial filtering of EEG at different frequencies. Evaluation with motor imagery recordings suggests that the circular Laplacian leads to a maximum of 5% improvement over the traditional discrete Laplacian in a motor imagery classification task