Multi-sensor Weighted Support Vector Machine Algorithm Oriented to P300 Signals
The P300 signal is widely used in brain computer interfaces(BCIs)because of its high recognition accuracy,flexible number of commands and short training time.Mapping P300 signals into control commands,namely,P300 signal processing is the research core of BCIs.Focusing on variability of raw data collected from different electrodes,a multi-sensor weighted support vector machine(msw-SVM)algorithm is proposed.It makes the amplitude difference of targets and non-targets signals more obvious to obtain better recognition accuracy.Experiments proved the classification result of this proposed method outperforms the traditional support vector machine(SVM)method.Meanwhile,as for P300 signal pre-processing,an optimal weighted averaging filter was employed to enhance the signal-to-noise ratio.It offers better data sources for signal processing.
P300 signal brain computer interface multi-sensor weighted support vector machine optimal weighted average filtering
Zemin Liu Haojie Liu Congsheng Zhang Kun Chen
School of Information Engineering,Wuhan University of Technology,Wuhan,China School of Information Engineering,Wuhan University of Technology,Wuhan,China;Key Laboratory of Fiber
国际会议
重庆
英文
2055-2059
2017-03-25（万方平台首次上网日期，不代表论文的发表时间）