Dimensionality Reduction of EEG for Classification using Mutual Information and SVM
(2011) IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2011) — Location: Beijing (China) (18.September.2011)
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- Abstract
- Dimensionality reduction is a well known technique in signal processing oriented to improve both the computational cost and the performance of classifiers. We use an electroencephalogram (EEG) feature matrix based on three extraction methods: tracks extraction, wavelets coefficients and Fractional Fourier Transform. The dimension reduction is performed by Mutual Information (MI) and a forward-backward procedure. Our results show that feature extraction and dimension reduction could be considered as a new alternative for solving EEG classification problems.
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Citations
Guerrero-Mosquera, C., Verleysen, M., & Navia Vazquez, A. (2011). Dimensionality Reduction of EEG for Classification using Mutual Information and SVM. Proceedings of the 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2011), 1-6. https://doi.org/10.1109/MLSP.2011.6064595