title: Study of Feature Classification Methods in BCI Based on Neural Networks.
abstract: 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.
authors.affiliation: College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, 300072, China; School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
authors.forename: Boqiang
authors.initials: B
authors.lastname: Liu
authors.forename: Mingshi
authors.initials: M
authors.lastname: Wang
authors.forename: Hongqiang
authors.initials: H
authors.lastname: Yu
authors.forename: Lanlan
authors.initials: L
authors.lastname: Yu
authors.forename: Zhongguo
authors.initials: Z
authors.lastname: Liu