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Singular value decomposition based feature classification for single trial brain-computer interface design

Andrews, S., Palaniappan, Ramaswamy, Kiong, L.C., Mastorakis, N. (2009) Singular value decomposition based feature classification for single trial brain-computer interface design. In: ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers. . pp. 46-53. ACM ISBN 978-960-474-099-4. (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
https://dl.acm.org/citation.cfm?id=1627712

Abstract

The performance of any brain-computer interface (BCI) highly depends on being artifact free. In this study, we propose a mathematical modelling approach to design an efficient non-invasive BCI based on P300 component found in single trial visual evoked potential (VEP) signals. Since the brain processes multiple functions simultaneously the extracted VEP results are in a complex pattern. Further, the characteristics of the P300 component are difficult to be determined a priori especially when the signals are analysed on single trial basis. However, the data used by BCI systems have high dimensionality due to the recording from multiple electrode locations and this high dimensionality could be exploited for reducing the effects from artifacts, using specific pre-processing techniques. In this research study, we propose a mathematical framework for noise reduction and a two-step classification using dynamic methods that results in an enhanced BCI design. The application of singular value decomposition (SVD) to the discrete single trial VEP data facilitates reduction of noise and operational data dimension. Frequency specific filtering further reduces noise and a computationally simple distance based measure with novel method of using two thresholds was utilised for classification. The experimental results give a very low false accept rate (FAR) and false reject rate (FRR) and a near negligible equal error rate (EER) of 2.91%. The high accuracy obtained validates our proposed single trial based approach.

Item Type: Conference or workshop item (Proceeding)
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - Proc. WSEAS Int. Conf. Comput. - Held part WSEAS CSCC Multiconference [Field not mapped to EPrints] AD - Faculty of Information Science and Technology, Multimedia University, Malacca, Malaysia [Field not mapped to EPrints] AD - Dept. of Computing and Electronic Systems, University of Essex, Colchester, United Kingdom [Field not mapped to EPrints] AD - Technical University of Sofia, Bulgaria [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Conference Paper [Field not mapped to EPrints] C3 - Proceedings of the 13th WSEAS International Conference on Computers - Held as part of the 13th WSEAS CSCC Multiconference [Field not mapped to EPrints]
Uncontrolled keywords: Brain-computer interface, P300 component, Single trial analysis, Singular value decomposition, Visual evoked potential, Apriori, Brain process, Complex pattern, Distance-based, Dynamic method, Equal error rate, False accept rate, False reject rate, Feature classification, High dimensionality, Mathematical frameworks, Mathematical modelling, Multiple electrodes, Multiple function, Noise reductions, Non-invasive, Novel methods, Operational data, P300 component, Pre-processing, Research studies, Single trial, Single-trial analysis, Visual evoked potential, Data reduction, Interfaces (computer), Optical sensors, Singular value decomposition, Brain computer interface
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 20:02 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70709 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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