Classifying EEG using incremental support vector machine in BCIs

  • Authors:

  • Xiaoming Zheng;Banghua Yang;Xiang Li;Peng Zan;Zheng Dong

  • Affiliations:

  • Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China;Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China and State Key Lab ...;Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China;Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China;Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China

  • Venue:
  • LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

The discrimination of movement imagery electroencephalography (EEG) is an essential issue in brain-computer interfaces (BCIs). Classifying EEG signals is an important step in the discrimination process. From the physiological standpoint, EEG signal varies with the time elapse, mood, tiredness of the subject, etc. An excellent classifier should be adaptive to tackle the dynamic variations of EEG. In this paper, an incremental support vector machine (ISVM) is adopted to classifying the EEG. The ISVM can consecutively delete some history samples and replenish some new samples obtained lately. And so the classifier model of the ISVM is updated periodically to adapt to the variations of EEG. At the same time, the ISVM can use a small training set to train the classifier, which is better in training speed and memory consuming than the standard SVM. To the data set 1 on left hand and foot imagery of BCI Competition IV 2008, the empirical mode decomposition (EMD) is employed to decompose the EEG signal into a series of intrinsic mode functions (IMFs), and then AR model parameters and instantaneous energy (IE) can be gained from some important IMFs, which form the initial features. The extracted features are fed into the ISVM classifier. Compared with the standard SVM, elementary results show that the ISVM can obtain better classification performance. The ISVM provides a good way to solve the adaptability of the online BCI system. Even so, the effectiveness of the ISVM should be verified furthermore with more data and subjects.