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Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach.
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Yang, Jianhua, Singh, Harsimrat, Hines, Evor, Schlaghecken, Friederike, Iliescu, Daciana, Leeson, Mark S. and Stocks, Nigel G. (2012) Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach. Artificial Intelligence In Medicine, Volume 55 (Number 2). pp. 117-126. doi:10.1016/j.artmed.2012.02.001 ISSN 1873-2860.
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Official URL: http://dx.doi.org/10.1016/j.artmed.2012.02.001
Abstract
We demonstrate that GNMM is able to perform effective channel selections/reductions, which not only reduces the difficulty of data collection, but also greatly improves the generalization of the classifier. An important step that affects the effectiveness of GNMM is the pre-processing method. In this paper, we also highlight the importance of choosing an appropriate time window position.
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