Classification of electroencephalogram signals using wavelet-CSP and projection extreme learning machine

Brain-computer interface (BCI) systems establish a direct communication channel from the brain to an output device. As the basis of BCIs, recognizing motor imagery activities poses a considerable challenge to signal processing due to the complex and non-stationary characteristics. This paper introdu...

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Bibliographic Details
Published in:Review of scientific instruments 2018-07, Vol.89 (7), p.074302-074302
Main Authors: Dai, Yixuan, Zhang, Xinman, Chen, Zhiqi, Xu, Xuebin
Format: Article
Language:English
Subjects:
Approximation
Electroencephalography
Human-computer interface
Imagery
Motors
Scientific apparatus & instruments
Signal classification
Signal processing
Wavelet analysis
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Summary:Brain-computer interface (BCI) systems establish a direct communication channel from the brain to an output device. As the basis of BCIs, recognizing motor imagery activities poses a considerable challenge to signal processing due to the complex and non-stationary characteristics. This paper introduces an optimal and intelligent method for motor imagery BCIs. Because of the robustness to noise, wavelet packet decomposition and common spatial pattern (CSP) methods were implemented to reduce the dimensions of preprocessed signals. And a novel and efficient classifier projection extreme learning machine (PELM) was employed to recognize the labels of electroencephalogram signals. Experiments have been performed on the BCI Competition Dataset to demonstrate the superiority of wavelet-CSP in BCI and the outperformance of the PELM-based method. Results show that the average recognition rate of PELM approaches approximately 70%, while the optimal rate of other methods is 72%, whose training time and classification time are relatively longer as 11.00 ms and 11.66 ms, respectively, compared with 4.75 ms and 4.87 ms obtained by using the proposed BCI system.
ISSN:0034-6748
1089-7623
DOI:10.1063/1.5006511