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Improving classification accuracy using intra-session classifier training and implementation for a BCI based on automated parameter selection

Syan, C.S., Harnarinesingh, R.E.S., Palaniappan, Ramaswamy (2012) Improving classification accuracy using intra-session classifier training and implementation for a BCI based on automated parameter selection. International Journal of Intelligent Systems Technologies and Applications, 11 (1-2). pp. 36-48. ISSN 1740-8865. (doi:10.1504/IJISTA.2012.046542) (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)

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Official URL
http://dx.doi.org/10.1504/IJISTA.2012.046542

Abstract

Genetic Algorithms (GAs) were used in a previous study to automate parameter selection for an EEG-based P300-driven Brain-Computer Interface (BCI). The GA approach showed marked improvement over data-insensitive parameter selection; however, it required lengthy execution times thereby rendering it infeasible for online implementation. Automated parameter selection is retained in this work; however, it is achieved using the less computationally intensive N-fold cross-validation (NFCV). Additionally, this study sought to improve BCI classification accuracy using a training data collection and application protocol that the authors refer to as 'Intra-session classifier training and implementation'. Intra-session classifier training and implementation using NFCV-driven automated parameter selection yielded a classification accuracy of 82.94% compared to 45.44% for the inter-session approach using datainsensitive parameters. These findings are significant impact since the intrasession protocol can be applied to any P300-based BCI regardless of its application platform to obtain improved classification accuracy. Copyright © 2012 Inderscience Enterprises Ltd.

Item Type: Article
DOI/Identification number: 10.1504/IJISTA.2012.046542
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - Int. J. Intell. Syst. Technol. Appl. [Field not mapped to EPrints] AD - Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, University of the West Indies, St. Augustine, Trinidad and Tobago [Field not mapped to EPrints] AD - School of Computer Science and Electronic Engineering, 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]
Uncontrolled keywords: Automation, BCI, FLDA, Genetic algorithm, N-fold cross-validation, P300
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 12 Dec 2018 22:10 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70704 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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