An ANFIS Method to Improve SSVEP-BCI Anti-blinking Stability

被引：0
|
作者：
Lu Z. [1 ]
Zhang X. [1 ,2 ]
Zhang L. [1 ]
Li H. [1 ]
Li R. [1 ,2 ]
机构：
[1] School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an
[2] Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an
来源：
Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2019年 / 39卷 / 04期
关键词：
Adaptive neuro-fuzzy inference system (ANFIS); Brain control interface (BCI); Electroencephalogram (EEG); Electrooculography artifact; Steady-state visual evoked potentials (SSVEP);
D O I：
10.16450/j.cnki.issn.1004-6801.2019.04.007
中图分类号：
学科分类号：
摘要：
The electrooculography (EOG) artifact removal for steady-state visual evoked potentials (SSVEP) is studied. An EOG artifact removal method for electroencephalogram (EEG) without ocular electrode based on adaptive neuro-fuzzy inference system (ANFIS) is proposed, and the experiments are carried out to prove the improvement of SSVEP-BCI's stability under random blinking. It tackles the stability of brain control interface (BCI) under artifact interference and takes the stability of SSVEP-BCI under random blinking as the key point. The proposed method approximates the nonlinear transforming function from the EOG source to EOG artifact with ANFIS to cancel the EOG artifact from EEG. The EOG artifact is deleted in an adaptive noise cancellation (ANC) after the source obtained from the prefrontal lobe's EEG passing through a tapped delay line (TDL) into the cancellation. All the parameters and functions are elaborated based on the experiments of steady-state visual stimulation under random blinking. The proposed method cancel EOG artifact in SSVEP while maintaining valid information on steady-state visual stimulation. The recognition accuracy is 6.25% and 10% higher than that of the classical band-pass filter and traditional ICA respectively, thus ensure the stability of SSVEP-BCI under random blinking. © 2019, Editorial Department of JVMD. All right reserved.
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页码：727 / 732
页数：5
相关论文
共 10 条
  • [1] Berger H., Uber das elektroenke-phalogramm des menschen, European Archives of Psychiatry & Clinical Neuroscience, 87, pp. 527-570, (1929)
  • [2] Zhang X., Li R., Li Y., Research and forecast on brain-computer interface, Journal of Vibration, Measurement & Diagnosis, 34, 2, pp. 205-211, (2014)
  • [3] Chen X., Wang Y., Nakanishi M., Et al., High-speed spelling with a noninvasive brain-computer interface, Proceedings of the National Academy of Sciences of the United States of America, 112, 44, (2015)
  • [4] Md K.I., Amir R., Zhi Y., Methods for artefact detection and removal from scalp EEG: a review, Clinical Neurophysiology, 46, pp. 287-305, (2016)
  • [5] Miguillon J., Lopez-G M.A., Pelayo F., Trends in EEG-BCI for daily-life: requirements for artefact removal, Biomedical Signal Processing & Control, 31, pp. 407-418, (2017)
  • [6] Cosandier-R D., Merlet I., Bartolomei F., Et al., Computational modeling of epileptic activity: from cortical sources to EEG signals, Journal of Clinical Neurophysiology Official Publication of the American Electroencephalographic Society, 27, 6, pp. 465-470, (2010)
  • [7] Hyvarinen A., Oja E., Independent component analysis: algorithms and applications, Neural Networks, 13, pp. 411-430, (2000)
  • [8] Barbati G., Porcaro C., Zappasodi F., Et al., Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals, Clinical Neurophysiology Official Journal of the International Federation of Clinical Neurophysiology, 115, 5, (2004)
  • [9] Chen J., Zhang X., Yin G., Human gait events fast recognition method via surface electromyography, China Mechanical Engineering, 27, 7, pp. 911-916, (2016)
  • [10] Li R., Zhang X., Zhang L., Et al., Brain-controlled prosthesis manipulation based on scene graph-SSVEP, Journal of Xi'an Jiaotong University, 51, 1, pp. 115-121, (2017)
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