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

Authors: Chanan S. Syan; Randy E.S. Harnarinesingh; Ramaswamy Palaniappan

Addresses: Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, The University of the West Indies, St. Augustine, Trinidad & Tobago. ' Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, The University of the West Indies, St. Augustine, Trinidad & Tobago. ' School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK

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 data-insensitive parameters. These findings are significant impact since the intra-session protocol can be applied to any P300-based BCI regardless of its application platform to obtain improved classification accuracy.

Keywords: P300; BCI; brain-computer interface; FLDA; genetic algorithms; GAs; N-fold cross-validation; classification accuracy; parameter selection; EEG; electroencephalographs.

DOI: 10.1504/IJISTA.2012.046542

International Journal of Intelligent Systems Technologies and Applications, 2012 Vol.11 No.1/2, pp.36 - 48

Received: 01 Oct 2011
Accepted: 30 Nov 2011

Published online: 16 Apr 2012 *

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