Comparison of Inter-and Intra-Subject Variability of P300 Spelling Dictionary in EEG Compressed Sensing

In this paper, we propose a new compression method for electroencephalographic signals based on the concept of compressed sensing (CS) for the P300 detection spelling paradigm. The method uses a universal mega-dictionary which has been found not to be patient-specific. To validate the proposed method, electroencephalography recordings from the competition for Spelling, BCI Competition III Challenge 2005 Dataset II, have been used. To evaluate the reconstructed signal, both quantitative and qualitative measures were used. For qualitative evaluation, we used the classification rate for the observed character based on P300 detection in the case of the spelling paradigm applied on the reconstructed electroencephalography signals, using the winning scripts (Alain Rakotomamonjy and Vincent Guigue). While for quantitative evaluation, distortion measures between the reconstructed and original signals were used. Keywords—Biomedical signal processing; Brain-computer interfaces; Compressed sensing; Classification algorithms; Electroencephalography