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Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing

Asensio-Cubero, J and Gan, JQ and Palaniappan, R (2013) 'Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing.' Biomedical Signal Processing and Control, 8 (6). 772 - 778. ISSN 1746-8094

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Abstract

Brain computer interfaces (BCI) provide a new approach to human computer communication, where the control is realised via performing mental tasks such as motor imagery (MI). In this study, we investigate a novel method to automatically segment electroencephalographic (EEG) data within a trial and extract features accordingly in order to improve the performance of MI data classification techniques. A new local discriminant bases (LDB) algorithm using common spatial patterns (CSP) projection as transform function is proposed for automatic trial segmentation. CSP is also used for feature extraction following trial segmentation. This new technique also allows to obtain a more accurate picture of the most relevant temporal-spatial points in the EEG during the MI. The results are compared with other standard temporal segmentation techniques such as sliding window and LDB based on the local cosine transform (LCT). © 2013 Elsevier Ltd.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Clare Chatfield
Date Deposited: 09 Sep 2014 12:33
Last Modified: 17 Aug 2017 17:52
URI: http://repository.essex.ac.uk/id/eprint/9228

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