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]





Design of Wearable Brain-Computer Interface System Based on Dry Electrode
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
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
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
embedded system wearable device brain-computer interface steady state visual evoked potential Pearson correlation coefficient
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。
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.


[1] KILICARSLAN A, PRASAD S, GROSSMAN R G, et al. High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton [C]∥2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway, NJ, USA: IEEE, 2013: 5606-5609.
[2] BUSCH N A, DUBOIS J, VANRULLEN R. The phase of ongoing EEG oscillations predicts visual perception [J]. Journal of Neuroscience, 2009, 29(24): 7869-7876.
[3] ANUMANCHIPALLI G K, CHARTIER J, CHANG E F. Speech synthesis from neural decoding of spoken sentences [J]. Nature, 2019, 568(7753): 493-498.
[4] TOMISLAV M, SARMA A A, DANIEL B, et al. Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals [J]. Journal of Neurophysiology, 2018, 120(1): 343-360.
[5] 徐光华, 张锋, 谢俊, 等. 稳态视觉诱发电位的脑机接口范式及其信号处理方法研究 [J]. 西安交通大学学报, 2015, 49(6): 1-7.
XU Guanghua, ZHANG Feng, XIE Jun, et al. Brain-computer interface paradigms and signal processing strategy for steady state visual evoked potential [J]. Journal of Xi’an Jiaotong University, 2015, 49(6): 1-7.
[6] CHEN X, WANG Y, NAKANISHI M, et al. High-speed spelling with a noninvasive brain-computer interface [J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(44): 6058-6067.
[7] BELWAFI K, ROMAIN O, GANNOUNI S, et al. An embedded implementation based on adaptive filter bank for brain-computer interface systems [J]. Journal of Neuroscience Methods, 2018, 305: 1-16.
[8] CHAI R, NAIK G R, LING S H, et al. Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems [J]. BioMedical Engineering OnLine, 2017, 16(1): 5.
[9] 支丹阳, 杜秀兰, 赵靖, 等. 基于便携式脑电信号采集器的脑-机器人交互系统 [J]. 电子测量与仪器学报, 2016, 30(5): 694-701.
ZHI Danyang, DU Xiulan, ZHAO Jing, et al. Brain-robot interaction system based on portable brain signal collector [J]. Journal of Electronic Measurement and Instrumentation, 2016, 30(5): 694-701.
[10] 张小栋, 郭晋, 李睿, 等. 表情驱动下脑电信号的建模仿真及分类识别 [J]. 西安交通大学学报, 2016, 50(6): 1-8.
ZHANG Xiaodong, GUO Jin, LI Rui, et al. A simulation model and pattern recognition method of electroencephalogram driven by expression [J]. Journal of Xi’an Jiaotong University, 2016, 50(6): 1-8.
[11] LIN C T, CHEN Y C, HUANG T Y, et al. Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver’s drowsiness detection and warning [J]. IEEE Transactions on Biomedical Engineering, 2008, 55(5): 1582-1591.
[12] SHYU K K, LEE P L, LEE M H, et al. Development of a low-cost FPGA-based SSVEP BCI multimedia control system [J]. IEEE Transactions on Biomedical Circuits and Systems, 2010, 4(2): 125-132.
[13] KIM D, BYUN W, KU Y, et al. High-speed visual target identification for low-cost wearable brain-computer interfaces [J]. IEEE Access, 2019, 7: 55169-55179.
[14] TEXAS INSTRUMENTS, ADS1299-x low-noise, 4-, 6-, 8-channel, 24-bit, analog-to-digital converter for EEG and biopotential measurements [EB/OL].(2017-01-15)[2019-04-29]. http: ∥www.ti.com/lit/ds/symlink/ads1299.pdf.
[15] SHEN M, SUN L, CHAN F H Y. Method for extracting time-varying rhythms of electroencephalography via wavelet packet analysis [J]. IEE Proceedings: Science, Measurement and Technology, 2001, 148(1): 23-27.
[16] 洪灿梅, 刘爱莲, 刘名扬, 等. FIR滤波器与IIR滤波器去噪效果对比研究 [J]. 微型机与应用, 2015, 34(21): 67-69.
HONG Canmei, LIU Ailian, LIU Mingyang, et al. Comparison and researches on FIR filter denoising and IIR filter denoising [J]. Microcomputer & Its Applications, 2015, 34(21): 67-69.
[17] 伏燕军, 程强强, 于润桥, 等. 信号FIR数字滤波后相位延迟的消除 [J]. 计算机工程与应用, 2012, 48(7): 146-149.
FU Yanjun, CHENG Qiangqiang, YU Runqiao. Eliminate phase delay of FIR filtered signal [J]. Computer Engineering and Applications, 2012, 48(7): 146-149.
[18] PFURTSCHELLER G, NEUPER C. Motor imagery and direct brain-computer communication [J]. Proceedings of the IEEE, 2001, 89(7): 1123-1134.
[19] CHENG M, GAO X, GAO S, et al. Design and implementation of a brain-computer interface with high transfer rates [J]. IEEE Transactions on Biomedical Engineering, 2002, 49: 1181-1186.
[20] WANG Y, WANG R, GAO X, et al. A practical VEP-based brain-computer interface [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006, 14(2): 234-240.
[21] LIN Z, ZHANG C, WU W, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs [J]. IEEE Transactions on Biomedical Engineering, 2006, 53(12): 2610-2614.
[22] BIN G, GAO X, YAN Z, et al. An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method [J]. Journal of Neural Engineering, 2009, 6(4): 046002.
[23] 笪铖璐, 陈志阳, 黄丽亚. 基于CCA的SSVEP性能研究 [J]. 计算机技术与发展, 2015(5): 52-55.
DA Chenglu, CHEN Zhiyang, HUANG Liya. Study on performance of SSVEP based on CCA [J]. Computer Technology and Development, 2015(5): 52-55.
[24] DI RUSSO F, SPINELLI D. Electrophysiological evidence for an early attentional mechanism in visual processing in humans [J]. Vision Research, 1999, 39(18): 2975-2985.
[25] WOLPAW J R, BIRBAUMER N, HEETDERKS W J, et al. Brain-computer interface technology: a review of the first international meeting [J]. IEEE Transactions on Rehabilitation Engineering, 2000, 8(2): 164-173.
[26] 杜智超, 陈志凯, 陈弘达, 等. 视觉感知空间分布对SSVEP调制成分的影响 [J]. 北京生物医学工程, 2014, 33(4): 337-343.
DU Zhichao, CHEN Zhikai, CHEN Hongda, et al. Effect of spatial distribution of visual perception on intermodulation SSVEP [J]. Beijing Biomedical Engineering, 2014, 33(4): 337-343.


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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