Abstract
A novel feature extraction method based on cloud model for EEG classification is proposed in the paper. The cloud model can transform numerical data to qualitative concept described by a group of characteristics. It provides a new way for concept induction in machine learning. Classification of single trial EEG recorded in a ‘self-paced key typing’ experiment is made through feature extraction based on cloud model and linear regression method for the classification of feature vectors. Results of up to 90% classification accuracy on test data set were obtained. The results show that compared with other methods, the feature extraction and translation method for EEG classification in this paper is simple and effective.
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Li, S., Shao, C. (2007). Classification of Single Trial EEG Based on Cloud Model for Brain-Computer Interfaces. In: Li, K., Li, X., Irwin, G.W., He, G. (eds) Life System Modeling and Simulation. LSMS 2007. Lecture Notes in Computer Science(), vol 4689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74771-0_38
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DOI: https://doi.org/10.1007/978-3-540-74771-0_38
Publisher Name: Springer, Berlin, Heidelberg
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