Feature classification is one of the important aspects in Brain-computer interfaces (BCI) system. It has been known that a higher precision can be achieved if use neutral networks in a proper way for feature classification. In this paper, three feature identification ways were introduced and discussed. In the experiment of left-right hand classification, the arithmetic of the small mean square difference is proposed and studied, so as to get a good converging in the task classification. The design method of input and output layer for the BP neural network was discussed. Experiment results show that it is a feasible processing algorithm to classify the different events.
Study of Feature Classification Methods in BCI Based on Neural Networks. Publishing Authors By Initials
Study of Feature Classification Methods in BCI Based on Neural Networks. Journal Published:
PUBLICATION TYPE: Journal Article
Journal: Conference proceedings : ... Annual International
VOLUME: 3
Page Numbers: 2932-5
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ISSN: 1557-170X
DAY: 6
MONTH: 02
YEAR: 2005
Study of Feature Classification Methods in BCI Based on Neural Networks. Information
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LANGUAGE: eng
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Grant and Affiliation Information for Study of Feature Classification Methods in BCI Based on Neural Networks.
AFFILIATION: College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, 300072, China; School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
Country: United States
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MEDLINETA: Conf Proc IEEE Eng Med Biol So
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