并行任务干扰下的ERP-BCI自适应拮抗方法研究

doi:10.3969/j.issn.0258-8021.2020.02.02

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Abstract ERP-BCI is a classic brain-computer interface paradigm, useing event-related potential (ERP) features to decode users thinking activities. Recent studies have found out that the decrease of ERP-BCI recognition rate induced by impacts of ERP features will happen if human brains control BCI simultaneously and perform other thinking activities. To explore solutions to this problem, this paper established an ERP-BCI interference antagonism method that applied dynamic stop criterion to adaptively adjust the stimulus repetition to maintain recognition performance. In the research, the working memory n-back task was used to construct the thinking interference task parallel to the ERP-BCI operation. The ERP data achieved from the task of no interference were used to establish the discriminant model and the dynamic stop algorithm which are employed in the operation of ERP-BCI online under different levels of the interference with dissimilar tasks. Ten subjects participated in the operation of ERP-BCI online. The experimental results showed that the proposed method of interference antagonism could obtain the character recognition rate without significant difference between interference and no interference (the average recognition rate reached 95%). This study provides a certain technical foundation for establishing highly robust ERP-BCI.

Key words： brain-computer interface event-related potential parallel thinking task dynamic stopping criterion

Received: 10 January 2019

PACS:

R318

Corresponding Authors: ^*E-mail: qhz@tju.edu.cn

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	Articles by authors
	Huang Yihao
	Chen Yuqian
	Qi Hongzhi

Cite this article:

Huang Yihao,Chen Yuqian,Qi Hongzhi. Research on Adaptive Antagonism Method of ERP-BCI Under Parallel Task Interference[J]. Chinese Journal of Biomedical Engineering, 2020, 39(2): 137-144.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2020.02.02 OR http://cjbme.csbme.org/EN/Y2020/V39/I2/137

[1] Wolpaw JR, Birbaumer N, Heetderks WJ, et al. Brain-computer interface technology: A review of the first international meeting[J]. IEEE Transactions on Rehabilitation Engineering, 2000, 8(2):164-173.
[2] Treder MS, Blankertz B. (C)overt attention and visual speller design in an ERP-based brain-computer interface[J]. Behavioral and Brain Functions, 2010, 6(1): 28.
[3] Birbaumer N, Cohen LG. Brain-computer interfaces: Communication and restoration of movement in paralysis[J]. The Journal of Physiology, 2007, 579(3): 621-636.
[4] Kramer AF, Sirevaag EJ, Braune R. A psychophysiological assessment of operator workload during simulated flight missions[J]. Human Factors, 1987, 29(2): 145-160.
[5] Ke Yufeng, Wang Peiyuan, Chen Yuqian, et al. Training and testing ERP-BCIs under different mental workload conditions[J]. Journal of Neural Engineering, 2015, 13(1): 016007.
[6] Schreuder M, Hhne J, Blankertz B, et al. Optimizing event-related potential based brain-computer interfaces: A systematic evaluation of dynamic stopping methods[J]. Journal of Neural Engineering, 2013, 10(3): 036025.
[7] Schreuder M, Rost T, Tangermann M. Listen, you are writing! Speeding up online spelling with a dynamic auditory BCI[J]. Frontiers in Neuroscience, 2011, 5: 112.
[8] Mainsah BO, Collins LM, Colwell KA, et al. Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study[J]. Journal of Neural Engineering, 2015, 12(1): 016013.
[9] Throckmorton CS, Colwell KA, Ryan DB, et al. Bayesian approach to dynamically controlling data collection in P300 spellers[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(3): 508-517.
[10] Wolpaw JR, Birbaumer N, McFarland DJ, et al. Brain-computer interfaces for communication and control[J]. Clinical Neurophysiology, 2002, 113(6): 767-791.
[11] Amiri S, Fazel-Rezai R, Asadpour V. A review of hybrid brain-computer interface systems[J]. Advances in Human-Computer Interaction, 2013, 2013: 1.
[12] Farwell LA, Donchin E. Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials[J]. Electroencephalography and Clinical Neurophysiology, 1988, 70(6): 510-523.
[13] Owen AM, McMillan KM, Laird AR, et al. N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies[J]. Human Brain Mapping, 2005, 25(1): 46-59.
[14] Fisher RA. The use of multiple measurements on taxonomic problems[J]. Annals of Human Genetics, 1936, 7(2): 179-188.
[15] Thurlings ME, Van Erp JB, Brouwer A-M, et al. Controlling a Tactile ERP-BCI in a Dual Task[J]. IEEE Transactions on Computational Intelligence and AI in Games, 2013, 5(2): 129-140.
[16] Kthner I, Wriessnegger S C, Müller-Putz G R, et al. Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain-computer interface[J]. Biological psychology, 2014, 102: 118-129.
[17] Kok A. Event-related-potential (ERP) reflections of mental resources: a review and synthesis[J]. Biological Psychology, 1997, 45(1-3): 19-56.
[18] Kok A. On the utility of P3 amplitude as a measure of processing capacity[J]. Psychophysiology, 2001, 38(3): 557-577.
[19] Getzmann S, Falkenstein M, Gajewski PD. Long-term cardiovascular fitness is associated with auditory attentional control in old adults: neuro-behavioral evidence[J]. PLoS ONE, 2013, 8(9): e74539.
[20] Pratt N, Willoughby A, Swick D. Effects of working memory load on visual selective attention: behavioral and electrophysiological evidence[J]. Frontiers in human neuroscience, 2011, 5: 57.
[21] Chen Yuqian, Ke Yufeng, Meng Guifang, et al. Enhancing performance of P300-Speller under mental workload by incorporating dual-task data during classifier training[J]. Computer Methods and Programs in Biomedicine, 2017, 152: 35-43.