Influence of Data Interval Optimization on SSVEP Algorithm Performance

doi:10.12068/j.issn.1005-3026.2021.08.005

Abstract

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

CLC Number:

R318

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.

References

[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.