[1]仲文远,李大海,张进华,等.可穿戴式干电极脑机接口系统设计[J].西安交通大学学报,2020,54(06):066-74.[doi:10.7652/xjtuxb202006009]
 ZHONG Wenyuan,LI Dahai,ZHANG Jinhua,et al.Design of Wearable Brain-Computer Interface System Based on Dry Electrode[J].Journal of Xi'an Jiaotong University,2020,54(06):066-74.[doi:10.7652/xjtuxb202006009]
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可穿戴式干电极脑机接口系统设计
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《西安交通大学学报》[ISSN:0253-987X/CN:61-1069/T]

卷:
54
期数:
2020年第06期
页码:
066-74
栏目:
出版日期:
2020-06-10

文章信息/Info

Title:
Design of Wearable Brain-Computer Interface System Based on Dry Electrode
文章编号:
0253-987X(2020)06-0066-09
作者:
仲文远1 李大海2 张进华1 王保增1 洪军1
1.西安交通大学现代设计与转子轴承系统教育部重点实验室, 710049, 西安; 2.西安航天动力试验技术研究所, 710100, 西安
Author(s):
ZHONG Wenyuan1 LI Dahai2 ZHANG Jinhua1 WANG Baozeng1 Hong Jun1
1. Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, Xi’an 710049, China; 2. Xi’an Aerospace Propulsion Test Technology Institute, Xi’an 710100, China
关键词:
嵌入式系统 可穿戴设备 脑机接口 稳态视觉诱发电位 皮尔逊相关系数
Keywords:
embedded system wearable device brain-computer interface steady state visual evoked potential Pearson correlation coefficient
分类号:
TP391
DOI:
10.7652/xjtuxb202006009
文献标志码:
A
摘要:
针对现有脑电设备便携性差的问题,提出了一种基于嵌入式的可穿戴干电极脑机接口(BCI)系统。该系统首先通过干电极配合24位模数转换芯片采集脑电信号,然后使用FIR数字滤波的方法进行3~35 Hz带通滤波,最后通过嵌入式处理器进行脑电识别。在识别算法方面,首先对脑电信号进行截断处理,去除视觉刺激延迟以及FIR滤波造成的群延迟; 然后采用皮尔逊相关系数法进行在线脑电识别,并分析刺激时长对正确率和信息传输率的影响。实验结果表明:该系统采集信号的平均信噪比为74.86 dB,50 Hz处共模抑制比为-132.57 dB,所用的相关系数法平均识别时间为0.13 s,四目标在线稳态视觉诱发电位实验的平均正确率为69.54%。与使用标准典型相关分析(CCA)算法的便携式BCI系统相比,该系统的平均识别时间缩短0.27 s,平均正确率提高了10%,可为干电极脑机接口系统应用提供思路。
Abstract:
An embedded wearable dry electrode brain-computer interface system is proposed to solve the problem of low portability of existing electroencephalogram(EEG)devices. The system first collects EEG signals through dry electrodes with a 24-bit analog-to-digital conversion chip and then uses an FIR digital filtering to perform 3 to 35 Hz bandpass filtering. At last, an embedded processor is used for EEG recognition. In terms of the recognition algorithm, EEG signals are truncated to eliminate the fixed delay of visual stimulation and the group delay caused by the FIR filtering; then the Pearson correlation coefficient method is used for online recognition, and the effects of stimulation duration on the correct rate and information transmission rate are analyzed. Experimental results show that the average signal-to-noise ratio of the collected signals in this system is 74.86 dB, and the common mode rejection ratio at 50 Hz is -132.57 dB. The average recognition time of the correlation coefficient method is 0.13 s, and the average accuracy of the online four-target steady-state visual evoked potential experiments is 69.54%. Compared with the portable BCI system using the standard CCA algorithm, the average recognition time is shortened by 0.27 s, and the average accuracy increases by 10%. These results show that the proposed system provides new ideas for applications of the dry electrodes BCI system.

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备注/Memo

备注/Memo:
收稿日期: 2019-12-13。作者简介: 仲文远(1994—),男,硕士生; 张进华(通信作者),男,教授,博士生导师。基金项目: “十三五”装备预研领域基金资助项目(61400030701)。
更新日期/Last Update: 2020-06-10