Abstract
Nonnegative matrix factorization (NMF) is a powerful feature extraction method for nonnegative data. This paper applies NMF to feature extraction for Electroencephalogram (EEG) signal classification. The basic idea is to decompose the magnitude spectra of EEG signals from six channels via NMF. Primary experiments on signals from one subject performing two tasks show high classification accuracy rate based on linear discriminant analysis. Our best results are close to 98% when training data and testing data from the same day, and 82% when training data and testing data from different days.
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Liu, W., Zheng, N., Li, X. (2004). Nonnegative Matrix Factorization for EEG Signal Classification. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_75
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DOI: https://doi.org/10.1007/978-3-540-28648-6_75
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