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Enhanced detection of visual-evoked potentials in brain-computer interface using genetic algorithm and cyclostationary analysis

Gupta, C.N., Palaniappan, Ramaswamy (2007) Enhanced detection of visual-evoked potentials in brain-computer interface using genetic algorithm and cyclostationary analysis. Computational Intelligence and Neuroscience, 2007 . ISSN 1687-5265. (doi:10.1155/2007/28692) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1155/2007/28692

Abstract

We propose a novel framework to reduce background electroencephalogram (EEG) artifacts from multitrial visual-evoked potentials (VEPs) signals for use in brain-computer interface (BCI) design. An algorithm based on cyclostationary (CS) analysis is introduced to locate the suitable frequency ranges that contain the stimulus-related VEP components. CS technique does not require VEP recordings to be phase locked and exploits the intertrial similarities of the VEP components in the frequency domain. The obtained cyclic frequency spectrum enables detection of VEP frequency band. Next, bandpass or lowpass filtering is performed to reduce the EEG artifacts using these identified frequency ranges. This is followed by overlapping band EEG artifact reduction using genetic algorithm and independent component analysis (G-ICA) which uses mutual information (MI) criterion to separate EEG artifacts from VEP. The CS and GA methods need to be applied only to the training data; for the test data, the knowledge of the cyclic frequency bands and unmixing matrix would be sufficient for enhanced VEP detection. Hence, the framework could be used for online VEP detection. This framework was tested with various datasets and it showed satisfactory results with very few trials. Since the framework is general, it could be applied to the enhancement of evoked potential signals for any application.

Item Type: Article
DOI/Identification number: 10.1155/2007/28692
Additional information: Unmapped bibliographic data: C7 - 28692 [EPrints field already has value set] LA - English [Field not mapped to EPrints] J2 - Comput. Intell. Neurosci. [Field not mapped to EPrints] AD - Department of Computing and Electronic Systems, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Article [Field not mapped to EPrints]
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 12 Dec 2018 22:33 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70723 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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