Feature Optimization in ERP-Based Brain Computer Interface Utilizing Adaptive Boosting

QI Hongzhi; SUN Changcheng; MING Dong; WAN Baikun; YIN Tao; LIU Zhipeng; REN Chaoshi

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(2): 296-300

QI Hongzhi, SUN Changcheng, MING Dong, et al., “Feature Optimization in ERP-Based Brain Computer Interface Utilizing Adaptive Boosting,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 296-300, 2013,

Citation:

QI Hongzhi, SUN Changcheng, MING Dong, et al., “Feature Optimization in ERP-Based Brain Computer Interface Utilizing Adaptive Boosting,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 296-300, 2013,

Citation:

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Feature Optimization in ERP-Based Brain Computer Interface Utilizing Adaptive Boosting

1.
Institute of Biomedical Engineering, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin 300192, China;
2.
Department of Biomedical Engineering, Tianjin University, Tianjin 300072, China

Funds: This work is supported by the National Natural Science Foundation of China (No.81127003, No.61172008, No.81171423, No.81222021), the National Key Technology R&D Program of the Ministry of Science and Technology of China (No.2012BAI34B02), the Tianjin Bureau of Public Health Foundation (No.09KY10, No.11KG108) and Program for New Century Excellent Talents in University of the Ministry of Education of China (No.NCET-10-0618).

Received Date: 2011-09-01
Rev Recd Date: 2011-09-01
Publish Date: 2013-04-25

Abstract

Abstract

In current studies on ERP-based brain computer interface, a big challenge is to find subjectspecified feature combination for more robust classification. In this paper we propose a recursive feature optimization method based on adaptive boosting and support vector machine to select optimal feature combination. The results of ERP-based brain computer spelling experiment on 11 subjects prove that AdaBoost-based optimization method can significantly improve classification accuracy and simultaneously depress feature dimension greatly. Meanwhile the computational complexity of optimization method is simplified by AdaBoost strategy for practical possibility.
- Event related potential (ERP),
- Brain computer interface (BCI),
- Adaptive boosting,
- Support vector machine (SVM),
- Recursive feature elimination

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References(18)

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