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The EEG Signal Classification in Compressed Sensing Space

Authors:
Monica Fira

Keywords: EEG; Compressed sensing; BCI; classification; P300

Abstract:
In this paper, it is analyzed the possibility of the classification of the compressed sensed electroencephalographic (EEG) signals. Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Shannon-Nyquist sampling theorem. The signals classification is done directly in the compressed space and the EEG signals reconstruction is not necessary. For testing we used EEG signals from a brain computer interface system used for a spelling paradigm. For the classification task, two methods were used, both based on machine learning, namely, Deep learning and Gradient boosting learning.

Pages: 5 to 10

Copyright: Copyright (c) IARIA, 2017

Publication date: July 23, 2017

Published in: conference

ISSN: 2308-4529

ISBN: 978-1-61208-571-5

Location: Nice, France

Dates: from July 23, 2017 to July 27, 2017