数据区间优化对SSVEP算法性能的影响

doi:10.12068/j.issn.1005-3026.2021.08.005

东北大学学报（自然科学版） ›› 2021, Vol. 42 ›› Issue (8): 1092-1097.DOI: 10.12068/j.issn.1005-3026.2021.08.005

数据区间优化对SSVEP算法性能的影响

段志豪，刘冲，陈杰，陆志国

(东北大学机械工程与自动化学院，辽宁沈阳110819)

修回日期:2020-11-27 接受日期:2020-11-27 发布日期:2021-09-02
通讯作者: 段志豪
作者简介:段志豪(1997-)，男，江西景德镇人，东北大学硕士研究生; 刘冲(1980-)，男，辽宁沈阳人，东北大学讲师，硕士生导师.
基金资助:
国家自然科学基金资助项目(51805074).

Influence of Data Interval Optimization on SSVEP Algorithm Performance

DUAN Zhi-hao， LIU Chong， CHEN Jie， LU Zhi-guo

School of Mechanical Engineering & Automation， Northeastern University， Shenyang 110819， China.

Revised:2020-11-27 Accepted:2020-11-27 Published:2021-09-02
Contact: LIU Chong
About author:-
Supported by:
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摘要/Abstract

摘要： 稳态视觉诱发电位(steady-state visual evoked potential， SSVEP)响应的个体差异性较大，不同环境下各被试者脑电信号的质量有差别.针对这个问题，研究了SSVEP中数据区间的优化对CCA(典型相关分析)和ECCA(扩展典型相关分析)方法分类结果的影响.首先通过网格搜索方法找到脑电信号的最优数据区间，然后使用CCA和ECCA方法对该区间数据进行特征识别，使得识别效果得到提升.实验结果表明，同时对数据区间起始点和终点进行优化能够有效提升信息传输率(ITR)，数据区间优化后的CCA和ECCA分类平均ITRs为(61.18±27.20)bit/min和(71.37±32.24)bit/min，比使用传统的仅优化数据区间终点的方法提高了29.89%和8.3%，证明了通过数据区间优化能够提升SSVEP算法的性能.

关键词: 脑电;脑-机接口;稳态视觉诱发电位;信息传输率;数据优化

Abstract: Steady-state visual evoked potential(SSVEP)responses vary greatly among individuals， which results in the quality difference of EEG signals from subjects in different environments. So， the effect of EEG data interval on CCA(canonical correlation analysis) and ECCA(extended canonical correlation analysis) classification results is investigated. The optimal data interval of EEG signals is determined through grid search， then， the EEG features in the optimal data interval are identified by CCA and ECCA， with the recognition results improved. The results show that the information transfer rate(ITR)can be effectively improved by optimizing the starting and ending points of the data interval. The average ITRs of CCA and ECCA classification after interval optimization are (61.18±27.20)bit/min and(71.37±32.24)bit/min， which are 29.89% and 8.3% higher than that of the traditional method which only optimizes the ending point of data interval. The results proved that the performance of SSVEP algorithm can be improved by optimizing data interval.

Key words: EEG; brain-computer interface; steady-state visual evoked potential; information transfer rate(ITR); data optimization

中图分类号:

R318

段志豪，刘冲，陈杰，陆志国. 数据区间优化对SSVEP算法性能的影响[J]. 东北大学学报（自然科学版）, 2021, 42(8): 1092-1097.

DUAN Zhi-hao， LIU Chong， CHEN Jie， LU Zhi-guo. Influence of Data Interval Optimization on SSVEP Algorithm Performance[J]. Journal of Northeastern University(Natural Science), 2021, 42(8): 1092-1097.

参考文献

[1]Saravanakumar D，Reddy M R.A brain computer interface based visual keyboard system using SSVEP and electrooculogram［M/OL］//Machine Learning and Metaheuristics Algorithms，and Applications［2020-10-10］.https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1007%2F978-981-15-4301-2_6.
[2]Nakanishi M，Wang Y J，Chen X G，et al.Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis［J］.IEEE Transactions on Biomedical Engineering，2018，65(1):104-112.
[3]Chai X K，Zhang Z M，Guan K，et al.A hybrid BCI-controlled smart home system combining SSVEP and EMG for individuals with paralysis ［J/OL］.Biomedical Signal Processing and Control，2020，56:101687［2020-10-15］.https://doi.org/10.1016/j.bspc.2019.101687.
[4]Yuan P，Gao X R，Allison B，et al.A study of the existing problems of estimating the information transfer rate in online brain-computer interfaces［J］.Journal of Neural Engineering，2013，10(2):026014.
[5]Chen X G，Wang Y J，Zhang S G，et al.Effects of stimulation frequency and stimulation waveform on steady-state visual evoked potentials using a computer monitor［J］.Journal of Neural Engineering，2019，16(6)［2020-10-05］.https://iopscience.iop.org/article/10.1088/1741-2552/ab2b7d.
[6]Chen X G，Chen Z K，Gao S K，et al.A high-ITR SSVEP-based BCI speller［J］.Brain-Computer Interfaces，2014，1(3/4):181-191.
[7]Chen X G，Wang Y J，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):E6058-E6067.
[8]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):1-7，108.(Xu Guang-hua，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):1-7，108.)
[9]Volosyak I.SSVEP-based Bremen-BCI interface—boosting information transfer rates［J/OL］. Journal of Neural Engineering，2011，8(3)［2020-10-05］.https://iopscience.iop.org/article/10.1088/1741-2560/8/3/036020.
[10]王春慧，江京，李海洋，等.基于动态自适应策略的SSVEP快速目标选择方法［J］.清华大学学报(自然科学版)，2018，58(9):788-795.(Wang Chun-hui，Jiang Jing，Li Hai-yang，et al.High-speed target selection method for SSVEP based on a dynamic stopping strategy［J］.Journal of Tsinghua University(Science and Technology)，2018，58(9):788-795.)
[11]Russo F D，Spinelli D.Electrophysiological evidence for an early attentional mechanism in visual processing in humans［J］. Vision Research，1999，39(18):2975-2985.
[12]Wu C H，Lakany H.The effect of the viewing distance of stimulus on SSVEP response for use in brain-computer interfaces［C］// IEEE International Conference on Systems，Man，and Cybernetics.Manchester，2013:1840-1845.
[13]etin V，Ozekes S，Varol H S.Harmonic analysis of steady-state visual evoked potentials in brain computer interfaces［J］.Biomedical Signal Processing and Control，2020，60:101999.
[14]Liu W Q，Zhang L，Li C S.A method for recognizing high-frequency steady-state visual evoked potential based on empirical modal decomposition and canonical correlation analysis ［C］//Proceedings of 2019 IEEE 3rd Information Technology，Networking，Electronic and Automation Control Conference.Chengdu，2019:774-778.
[15]Wang Y J，Nakanishi M，Wang Y T，et al.Enhancing detection of steady-state visual evoked potentials using individual training data［C/OL］// 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society［2020-10-20］.https://dx.doi.org/10.1109/EMBC.2014.6944263.
[16]Wong C M，Wan F，Wang B Y，et al.Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs［J］.Journal of Neural Engineering，2020，17(1):016026.

数据区间优化对SSVEP算法性能的影响

Influence of Data Interval Optimization on SSVEP Algorithm Performance

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