Abstract
Electroencephalogram (EEG) is a comprehensive indicator of human physiological activities. Because of its comprehensiveness and complexities, an electrode-covered collection device on the scalp cannot collect the discharge phenomenon of the activated brain area exhaustively. A single electrode analysis will miss a lot of important association information between different brain regions. This paper presents a novel strategy to solve this problem by combining the optimal electrodes. The whole method is divided into five steps: (1) input multi-electrodes EEG data, (2) utilize the Principal Component Analysis (PCA) method to obtain the optimal electrodes, (3) adopt the modified optimal electrodes recombination strategy and obtain optimal electrode combination strategy, (4) extract features by using Empirical Mode Decomposition (EMD), and (5) use Naive Bayes classifier to do the classification tasks. In order to evaluate the validity of the proposed method, we apply the proposed method in BCI Competition II datasets Ia. Experimental results show that our method improves recognition performance for BCI Competition II datasets Ia and the modified optimal electrodes recombination strategy is reasonable. This provides a new idea for analyzing the EEG features of other tasks.
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Acknowledgements
This research is partially sponsored by Natural Science Foundation of China (Nos. 61672070, 81471770, 61572004), the Beijing Municipal Natural Science Foundation (grant number 4182005).
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Duan, L., Cui, S., Liu, L., Qiao, Y. (2018). A Novel EEG Signal Recognition Method Using Modified Optimal Electrodes Recombination Strategy. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_52
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