An extended binary subband canonical correlation analysis detection algorithm oriented to the radial contraction-expansion motion steady-state visual evoked paradigm

Yuxue Zhao, Hongxin Zhang, Yuanzhen Wang, Chenxu Li, Ruilin Xu, Chen Yang
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引用次数: 2

Abstract

The radial contraction-expansion motion paradigm is a novel steady-state visual evoked experimental paradigm, and the electroencephalography (EEG) evoked potential is different from the traditional luminance modulation paradigm. The signal energy is concentrated chiefly in the fundamental frequency, while the higher harmonic power is lower. Therefore, the conventional steady-state visual evoked potential recognition algorithms optimizing multiple harmonic response components, such as the extended canonical correlation analysis (eCCA) and task-related component analysis (TRCA) algorithm, have poor recognition performance under the radial contraction-expansion motion paradigm. This paper proposes an extended binary subband canonical correlation analysis (eBSCCA) algorithm for the radial contraction-expansion motion paradigm. For the radial contraction-expansion motion paradigm, binary subband filtering was used to optimize the weighting coefficients of different frequency response signals, thereby improving the recognition performance of EEG signals. The results of offline experiments involving 13 subjects showed that the eBSCCA algorithm exhibits a better performance than the eCCA and TRCA algorithms under the stimulation of the radial contraction-expansion motion paradigm. In the online experiment, the average recognition accuracy of 13 subjects was 88.68% ± 6.33%, and the average information transmission rate (ITR) was 158.77 ± 43.67 bits/min, which proved that the algorithm had good recognition effect signals evoked by the radial contraction-expansion motion paradigm.
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一种面向径向收缩-扩张运动稳态视觉诱发范式的扩展二进制子带正则相关分析检测算法
径向收缩-扩张运动范式是一种新的稳态视觉诱发实验范式,脑电图(EEG)诱发电位不同于传统的亮度调制范式。信号能量主要集中在基频,而高次谐波功率较低。因此,传统的优化多个谐波响应分量的稳态视觉诱发电位识别算法,如扩展正则相关分析(eCCA)和任务相关分量分析(TRCA)算法,在径向收缩-扩张运动范式下的识别性能较差。本文提出了一种适用于径向收缩-扩张运动范式的扩展二进制子带规范相关分析(eBSCCA)算法。对于径向收缩-扩张运动范式,使用二元子带滤波来优化不同频率响应信号的加权系数,从而提高EEG信号的识别性能。涉及13名受试者的离线实验结果表明,在径向收缩-扩张运动范式的刺激下,eBSCCA算法表现出比eCCA和TRCA算法更好的性能。在在线实验中,13名受试者的平均识别准确率为88.68%±6.33%,平均信息传输率(ITR)为158.77±43.67比特/分钟,证明该算法对径向收缩-扩张运动范式诱发的信号具有良好的识别效果。
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审稿时长
10 weeks
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