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Spatio-spectral & temporal parameter searching using class correlation analysis and particle swarm optimization for a brain computer interface

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Satti, A. R., Coyle, D and Prasad, G (2009) Spatio-spectral & temporal parameter searching using class correlation analysis and particle swarm optimization for a brain computer interface. In: 2009 IEEE International Conference on Systems, Man and Cybernetics, San Antonio, USA. IEEE. 6 pp. [Conference contribution]

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URL: http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5346679&queryText%3DSpatio-spectral+.AND.+temporal+parameter+searching+using+class+correlation+analysis+and+particle+swarm+optimization+for+a+brain+computer+interface%26openedRefinements%3D*%

DOI: 10.1109/ICSMC.2009.5346679

Abstract

Distinct features play a vital role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, numerous parameters, such as separable frequency bands, data acquisition channels and time point of maximum separability are chosen explicit to each subject. Recent research has shown that using subject specific parameters for the extraction of invariant characteristics specific to each brain state can significantly improve the performance and accuracy of a brain-computer interface (BCI). This paper focuses on developing a fast autonomous user-specific tuned BCI system using particle swarm optimization (PSO) to search for optimal parameter combination based on the analysis of the correlation between different classes i.e., the R-squared (R2) correlation coefficient rather than assessing overall systems performance via performance measure such as classification accuracy. Experimental results utilizing eight subjects are presented which demonstrate the effectiveness of the proposed methods for fast & efficient user-specific tuned BCI system.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:12216
Deposited By:Professor Girijesh Prasad
Deposited On:09 Mar 2010 12:21
Last Modified:15 Mar 2011 16:05

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