A Comparative Study of Canonical Correlation Analysis and Power Spectral Density Analysis for SSVEP Detection

摘要：

Steady-state visual evoked potentials (SSVEPs) are widely employed for target detection in braincomputer interfaces (BCIs). Canonical correlation analysis (CCA), which extends ordinary correlation to two sets of variables, is a new method for SSVEP detection. In this paper, the performance of CCA is compared with that of traditional power spectral density analysis (PSDA) in terms of power spectral amplitude, recognition accuracy, information transfer rate and operating speed. The results show that the CCA method outperforms the PSDA in all these technical indexes.

关键词： brain-computer interface steady-state visual evoked potentials canonical correlation analysis power spectral density analysis

作者: Qingguo Wei Meixia Xiao Zongwu Lu

作者单位: Department of Electronic Engineering, Nanchang University, Nanchang 330031, China

会议类型: 国际会议

会议名称: 2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics 第三届智能人机系统与控制论国际会议 IHMSC 2011

会议地点: 杭州

会议语种:英文

页码: 242-245

在线出版日期: 2011-08-26（万方平台首次上网日期，不代表论文的发表时间）