Applying Probabilistic Classification Vector Machines in Self-paced BCI to Enhance Foot Movement Detection

Mohammadi, Rahele; Mahloojifar, Ali

doi:10.22041/ijbme.2013.13079

Document Type : Full Research Paper

Authors

Rahele Mohammadi ¹
Ali Mahloojifar ²

¹ Ph.D Student, Biomedical Eng. Department, Electrical and Computer Eng. College, TarbiatModares University

² Associate Professor, Biomedical Eng. Department, Electrical and Computer Eng. College, TarbiatModares University

10.22041/ijbme.2013.13079

Abstract

A critical issue in designing a self-paced brain computer interface (BCI) system is onset detection of the mental task from the continuous electroencephalogram (EEG) signal to produce a brain switch. This work shows significant improvement in a movement based self-paced BCI by applying a new sparse learning classification algorithm, probabilistic classification vector machines (PCVMs) to classify EEG signal. Constant-Q filters instead of constant bandwidth filters for frequency decomposition are also shown to enhance the discrimination of movement related patterns from EEG patterns associated with idle state. Analysis of the data recorded from seven subjects executing foot movement using the constant-Q filters and PCVMs shows a statistically significant 16% (p<0.03) average improvement in true positive rate (TPR) and a 2% (p<0.03) reduction in false positive rate (FPR) compared with applying constant bandwidth filters and SVM classifier.

Keywords

Main Subjects

References

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Iranian Journal of Biomedical Engineering

Applying Probabilistic Classification Vector Machines in Self-paced BCI to Enhance Foot Movement Detection

References

References

Volume 7, Issue 1 - Serial Number 1
June 2013
Pages 39-55

Applying Probabilistic Classification Vector Machines in Self-paced BCI to Enhance Foot Movement Detection

References

References

Volume 7, Issue 1 - Serial Number 1June 2013Pages 39-55

Volume 7, Issue 1 - Serial Number 1
June 2013
Pages 39-55