Bagging regularized common spatial pattern with hybrid motor imagery and myoelectric signal
Hongchuan Liu, Yali Li, Hongma Liu, Shengjin Wang
Abstract
Common Spatial Pattern(CSP) is a widely used algorithm in BCI application. However, it is sensitive to noise and artifact. In this paper, we propose a bagging regularized common spatial pattern (Bagging RCSP) approach for BCI with hybrid motor imagery and myoelectric signal. We divide the training samples into packets and choose training packets by Bagging to extract RCSP features. Furthermore, LDA is used to project the feature vector to lower space. In the end, a classification algorithm based on NNC is adopted. The Off-line experiment on BCI competition III attests Bagging RCSP versatile. The accuracy increases by 3%-5% in average than RCSP-A results. Furthermore, we designed and realized an online BCI system based on Bagging RCSP and evaluated through experiment involving four experimenters performing the BCI system of catching the apples. The results show the effectiveness of the proposed approach and the real time BCI system.
BibTeX
@inproceedings{icassp2016_baggingregulariz,
title = {Bagging regularized common spatial pattern with hybrid motor imagery and myoelectric signal},
author = {Hongchuan Liu and Yali Li and Hongma Liu and Shengjin Wang},
booktitle = {ICASSP 2016},
year = {2016}
}