Discrete Cosine Transform for MEG Signal Decoding

Abstract

In this study, we propose the discrete cosine transform coefficients as a new and effective set of features for recognizing patterns of brain activity in MEG recording. We claim that computing DCT coefficients on the time-frequency representation of MEG signals is an efficient technique to reduce the dimensionality of feature space without losing discriminative power in brain decoding tasks. Our classification results on single-trial MEG decoding suggest that DCT is a viable method comparing to standard methods and it improves decoding accuracy by preserving the dynamic patterns of signal in time, frequency and space domains.