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Genetic feature selection to optimally detect P300 in brain computer interfaces | IEEE Conference Publication | IEEE Xplore

Genetic feature selection to optimally detect P300 in brain computer interfaces


Abstract:

A Brain Computer Interface is a system that provides an artificial communication between the human brain and the external world. The paradigm based on event related evoke...Show More

Abstract:

A Brain Computer Interface is a system that provides an artificial communication between the human brain and the external world. The paradigm based on event related evoked potentials is used in this work. Our main goal was to efficiently solve a binary classification problem: presence or absence of P300 in the registers. Genetic Algorithms and Support Vector Machines were used in a wrapper configuration for feature selection and classification. The original input patterns were provided by two channels (Oz and Fz) of resampled EEG registers and wavelet coefficients. To evaluate the performance of the system, accuracy, sensibility and specificity were calculated. The wrapped wavelet patterns show a better performance than the temporal ones. The results were similar for patterns from channel Oz and Fz, together or separated.
Date of Conference: 31 August 2010 - 04 September 2010
Date Added to IEEE Xplore: 11 November 2010
ISBN Information:

ISSN Information:

PubMed ID: 21096616
Conference Location: Buenos Aires, Argentina

References

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