EEG Signals Classification Based on AR Model and SVM Algorithm

  • HUANG Lu;LI Ran;GU Jun
  • College of Information Engineering, Dalian Ocean University, Dalian 116023, Liaoning Province, China

Received date: 2013-06-19

  Revised date: 2013-10-31

  Online published: 2013-12-18


In P300 based brain-computer interface (BCI), the effective feature exaction and classification of P300 is the key to carry out the follow-up work. An electroencephalogram (EEG) classification method combining with autoregressive (AR) model and support vector machine (SVM) was proposed. For 10 channels EEG data, AR model was built up for each epoch. The estimation of AR coefficients was taken on using least square method and the estimated coefficient sequences constituted the feature vectors. SVM was used as classifier and dataset Ⅱ of BCI Competition Ⅲ was used to verify this method. The recognition accuracy arrived at 93.5% with 15 times stimulations. The experimental results and data analysis show that the method using SVM to classify the feature vectors composed of AR coefficient sequences owns satisfactory recognition accuracy. It lays good comparison theory and experimental basis for the realization of P300 based BCI.

Cite this article

HUANG Lu;LI Ran;GU Jun . EEG Signals Classification Based on AR Model and SVM Algorithm[J]. Science & Technology Review, 2013 , 31(35) : 24 -27 . DOI: 10.3981/j.issn.1000-7857.2013.35.003