Abstract
EEG reflects the strength of the neuronal activity in the brain. Since EEG signal is weak, noisy and mixed with a large number of artifacts, which causes interference to the processing and identification of the EEG signal. Using EEG related pretreatment can effectively remove artifact, noise, and improve EEG signal-noise ratio and efficient, which provides more accurate data for feature extraction and classification. In this paper, we introduce several methods including PCA, ICA and CSP. Based on these methods, the complete process of EEG signal de-noising, feature extraction and classification are established, which can complete the classification and recognition of the motor imagery signals. We use a combination of a lot of pretreatment methods to analysis and process motor imagery of EEG and propose an improved algorithm named CS-CSP. The experimental results show that the Chebyshev type II filter is superior to the conventional pre-treatment methods and the recognition accuracy of CS-CSP is higher than CSP.
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Lan, Z., Liu, Y., Sourina, O., et al.: Real-time EEG-based user’s valence monitoring. In: 2015 10th International Conference on Information, Communications and Signal Processing (ICICS), pp. 1–5. IEEE (2015)
Stewart, A.X., Nuthmann, A., Sanguinetti, G.: Single-trial classification of EEG in a visual object task using ICA and machine learning. J. Neurosci. Methods 228, 1–14 (2014)
Lan, Z., Sourina, O., Wang, L., et al.: Real-time EEG-based emotion monitoring using stable features. Visual Comput. 32(3), 347–358 (2016)
Stikic, M., Johnson, R.R., Tan, V., et al.: EEG-based classification of positive and negative affective states. Brain-Comput. Interfaces 1(2), 99–112 (2014)
Subasi, A., Gursoy, M.I.: EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl. 37(12), 8659–8666 (2010)
Hou, X., Liu, Y., Sourina, O., et al.: EEG based stress monitoring. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3110–3115. IEEE (2015)
Fukunaga, K.: Instruction to Statistical Pattern Recognition. Elsevier, Orlando (1972)
Blankertz, B., Dornhege, G., Müller, K.R., et al.: Results of the BCI Competition III. BCI Meeting (2005)
Acknowledgments
The research work is supported by National Natural Science Foundation of China (U1433116) and the Fundamental Research Funds for the Central Universities (NP2017208).
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Zhu, Y., Wang, Z., Dai, C., Pi, D. (2017). Artifact Removal Methods in Motor Imagery of EEG. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_32
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DOI: https://doi.org/10.1007/978-3-319-68935-7_32
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