Lossless compression of electroencephalographic (EEG) data

The lossless compression of electroencephalographic (EEG) data is of great interest to the biomedical research community. In this paper, a two-stage technique of lossless compression involving decorrelating the sample points of the EEG signal and then entropy coding the resulting signal is examined. Two alternatives are presented for performing the first task. Specifically, the first stage consists either of a fixed coefficient filter or a recursive least squares lattice filter. The second stage employs arithmetic coding to perform the task of entropy coding the data. In the decompression stage, exact inverse filters are applied to achieve lossless compression. Simulations demonstrate the feasibility of this method for lossless EEG data compression.