Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach.
Yang, Jianhua, Singh, Harsimrat, Hines, Evor L., Schlaghecken, Friederike, Iliescu, Daciana, Leeson, Mark S., 1963- 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, Vol.55 (No.2). pp. 117-126. ISSN 1873-2860Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.artmed.2012.02.001
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.
|Item Type:||Journal Article|
|Subjects:||R Medicine > R Medicine (General)
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
T Technology > TA Engineering (General). Civil engineering (General)
|Divisions:||Faculty of Science > Engineering
Faculty of Science > Psychology
|Journal or Publication Title:||Artificial Intelligence In Medicine|
|Number of Pages:||10|
|Page Range:||pp. 117-126|
|Access rights to Published version:||Restricted or Subscription Access|
|Funder:||Economic and Social Research Council (Great Britain) (ESRC), Warwick Postgraduate Research Fellowship (WPRF), UK Overseas Research Students Awards Scheme (ORSAS), Warwick Institute of Advanced Study (IAS)|
|Grant number:||RES-000-22-1841 (ESRC)|
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