脑机接口中高频稳态视觉诱发电位的识别研究  

Recognition of high-frequency steady-state visual evoked potential for brain-computer interface

作　　者：罗睿心 豆心怡 肖晓琳[1,2] 吴乔逸 许敏鹏 明东[1,2] LUO Ruixin;DOU Xinyi;XIAO Xiaolin;WU Qiaoyi;XU Minpeng;MING Dong(School of Precision Instrument and Opto-electronics Engineering,Tianjin University,Tianjin 300072,P.R.China;Academy of Medical Engineering and Translational Medicine,Tianjin University,Tianjin 300072,P.R.China)

机构地区：[1]天津大学精密仪器与光电子工程学院,天津300072 [2]天津大学医学工程与转化医学研究院,天津300072

出　　处：《生物医学工程学杂志》2023年第4期683-691,共9页Journal of Biomedical Engineering

基　　金：国家自然科学基金(62122059,61976152,81925020,62106170);济南市“新高校20条”引进创新团队项目(2021GXRC071)。

摘　　要：利用高频刺激进行编码能够缓解基于稳态视觉诱发电位(SSVEP)的脑-机接口(BCI)产生的用户视觉疲劳,提升系统的舒适度和安全性,具有广阔的应用前景。然而,当前先进的SSVEP解码算法大多在低频数据集上进行对比验证,在高频SSVEP信号上的识别性能仍然未知。针对此问题,本文采集了20名受试者在高频SSVEP范式下的脑电(EEG)数据,对目前主流的2种典型相关分析算法、3种集成任务相关成分分析算法和1种任务判别成分分析算法展开对比。结果表明,它们均能有效解码高频SSVEP信号,且在不同条件下算法的分类性能指标和速度存在差异。本研究为高频SSVEP-BCI系统的算法选择提供了依据,在构建舒适友好型BCI系统方面具有潜在的应用价值。Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the braincomputer interface(BCI)based on steady-state visual evoked potential(SSVEP).It would improve the comfort and safety of the system and has promising applications.However,most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets,and their recognition performance on high-frequency SSVEPs was still unknown.To address the aforementioned issue,electroencephalogram(EEG)data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm.Then,the state-of-the-art SSVEP algorithms were compared,including 2 canonical correlation analysis algorithms,3 task-related component analysis algorithms,and 1 task discriminant component analysis algorithm.The results indicated that they all could effectively decode high-frequency SSVEPs.Besides,there were differences in the classification performance and algorithms'speed under different conditions.This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI,demonstrating its potential utility in developing user-friendly BCI.

关 键 词：脑-机接口 稳态视觉诱发电位 高频 解码算法 

分 类 号：R318[医药卫生—生物医学工程] TN911.7[医药卫生—基础医学]