Applying Chernoff Weighted Classification Frame Method to MotorImagery Brain Computer Interface

Ping TAN; Limei LIU; Fan GUO; Kaijun ZHOU

doi:10.11999/JEIT181132

Volume 42 Issue 2

Feb. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(2): 488-494

Ping TAN, Limei LIU, Fan GUO, Kaijun ZHOU. Applying Chernoff Weighted Classification Frame Method to MotorImagery Brain Computer Interface[J]. Journal of Electronics & Information Technology, 2020, 42(2): 488-494. doi: 10.11999/JEIT181132

Citation:

Ping TAN, Limei LIU, Fan GUO, Kaijun ZHOU. Applying Chernoff Weighted Classification Frame Method to MotorImagery Brain Computer Interface[J]. Journal of Electronics & Information Technology, 2020, 42(2): 488-494. doi: 10.11999/JEIT181132

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Applying Chernoff Weighted Classification Frame Method to MotorImagery Brain Computer Interface

doi: 10.11999/JEIT181132

1.
Key Laboratory of Hunan Province for New Retail Virtual Reality Technology, Hunan University ofTechnology and Business, Changsha 410205, China
2.
School of Automation, Central South University, Changsha 410083, China

Funds: The National Natural Science Foundation of China (61502537), The National Social Science Foundation of China (19BGL111), The Scientific Research Project of the Education Department of Hunan Province (18B338), The Open Fund of Key Laboratory of Hunan Province (2017TP1026), The Foundation of Ministry of Education Humanities and Social Sciences (14YJCZH099)

Received Date: 2018-12-07
Rev Recd Date: 2019-07-20

Available Online: 2019-09-11

Publish Date: 2020-02-19

Abstract

Abstract

For the problem that the classifier is less considered to be combined with the brain's cognitive process in the Brain-Computer Interface (BCI) system, a Chernoff-weighted based classifier integrated frame method is proposed and used in synchronous motor imagery BCI. In the method, the statistic characteristics of ElectroEncephaloGraphy (EEG) are obtained by analyzing in each time point of synchronous BCI, and then the probability model is established to compute the Chernoff error bound, which is adopted as the weight of common classifier to take the discriminant process. The test experiments are based on the datasets from BCI competitions, and the proposed frame method is employed to compose with LDA, SVM, ELM respectively. The experimental results demonstrate that the proposed frame method shows competitive performance compared with other methods.
- Brain Computer Interface (BCI),
- Motor imagery,
- Probabilistic model,
- Chernoff error bound,
- Pattern classification

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References(16)

References

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