Discrimination between Idle and Work States in BCI based on SSVEP

摘要：

We present a novel method for idle and work states classification in brain computer interface (BCI) based on steady-state visual evoked potentials (SSVEP). Canonical correlation analysis (CCA) and maximum contrast combination (MCC) are used to extract features of electroencephalogram (EEG) signals. The correlation coefficients from CCA and SNR from MCC were classified by a linear classifier. Then an extra strategy of excluding alpha wave interference helped improve the classification accuracy. This method had a good performance in real EEG signals.

关键词： Brain Computer Interface Canonical Correlatoin Analysis Maximum Contrast Combination Alpha Wave Detection

作者: Niya Wang Tianyi Qian Qing Zhuo Xiaorong Gao

作者单位: Tsinghua University Beijing China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 355-358

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）