A Novel BCI Classifier Based on Autoregressive Model and Support Vector Machine

Lu Huang; Jun Gu; Ran Li; Xiang Jun Li; Hong Yu

doi:10.4028/www.scientific.net/AMR.694-697.2522

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 694-697A Novel BCI Classifier Based on Autoregressive...

A Novel BCI Classifier Based on Autoregressive Model and Support Vector Machine

Abstract:

The efficient featureextraction and classification are very crucial for brain computerinterface(BCI) system. In this paper, feature extraction and classification forP300, a kind of EEG characteristic potential, was conducted. Afterpreprocessing EEG signals, we used autoregressive(AR) model for featureextraction, segmenting the selected EEG channel data and building AR model foreach segment respectively. AR model coefficients were estimated by using leastsquare method, and the estimated coefficient sequence constituted the featurevector. We applied support vector machine(SVM) for classification andexperimented on real EEG dataset. The experimental results showed the proposedmethod had a good recognition accuracy, being worth researching in the field of BCI.

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Periodical:

Advanced Materials Research (Volumes 694-697)

Pages:

2522-2525

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.694-697.2522

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Online since:

May 2013

Authors:

Lu Huang, Jun Gu, Ran Li, Xiang Jun Li, Hong Yu

Keywords:

Autoregressive Model (AR Model), Brain Computer Interface (BCI), Pattern Classification, Support Vector Machine (SVM)

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