基于运动起始视觉响应的在线实用化脑—机接口
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摘要
脑-机接口技术是新发展起来的,多通道人机交互的一种新形式。与之前的人机交互形式不同的是,脑-机接口不依赖于人的正常输出通道(外周神经和肌肉组织等),使人脑能够直接和外部环境进行信息交互。尽管脑-机接口系统的概念在很早就被实现,然而在迈向实用化的过程中还有很多问题有待解决。比如系统配置的复杂性,在线系统的自适应性,界面设计是否能满足日常环境需求等,都是实用化中需要考虑的问题。
运动起始视觉诱发电位(motion onset visual evoked potential,mVEP)近年来首次被本实验室引入到脑-机接口的研究中。在此基础上,本文系统地研究了mVEP的生理特性,比如不应期效应,刺激模式以及对比度对基于mVEP的脑-机接口系统的影响,为系统设计提供了基础。论文还针对N200成分在不同对比度下的稳定性提出了相应的导联选择方案,使得基于mVEP的脑-机接口系统在低对比度下也能获得稳定的性能。
脑电信号的特征提取和模式分类是脑-机接口系统的核心。本文对现有二分类的支持向量机(SVM)做出了改进,使其能够更好地实现对脑-机接口中不平衡样本的分类。本文中还提出了基于判决时间期望和基于判决准确率期望的两种自适应决策准则,有效地提高了在线脑-机接口的性能。本文提出了基于一类SVM的的异步脑-机接口算法,并通过对后验概率模型的推广,实现了对“空闲状态”的概率估计和检测。
基于论文中的研究和算法,我们开发出了首个基于mVEP的在线单导联的脑-机接口。12位受试者使用该系统的平均信息传输率达到42.1比特/分钟。此外,我们还在此基础上开发了一套网页浏览和搜索系统。实验中所有12位受试者都能够实现对该系统自如的操作,证明了该系统能够应用于实际的场景,嵌入并适应不同的界面,最终实现高效率的脑机交互。
最后,我们采用独立分量分析深入分析了选择性注意下的mVEP成分,并验证了基于mVEP的独立脑-机接口的可行性。
Brain-computer interface (BCI) is a newly developed approach of the multi-modal human-computer interaction. Different from the previous human-computer interaction, BCI could provide a non-muscular output, which allows direct interaction between human brain and the outside environment. Although the proof-of-concept of BCI systems was implemented decades ago, several major challenges are still to be addressed when moving towards a practical online BCI system, such as ease of the system setup, the adaptability of an online system, and the design of the user interface when presented in a real-world application.
Motion onset visual evoked potential (mVEP) has been introduced into BCI research recently by our group. Based on our previous studies, this paper intensively studied the properties that could affect the performance of the mVEP-based BCI, such as the refractory effect, the stimulus pattern, and the background contrast. Specifically, based on the robust N200 component in varied contrast, and the corresponding strategy of channel selection, the proposed mVEP-based BCI system could achieve a stable accuracy even in a low contrast.
Feature extraction and classification of the EEG are critical factors in the online BCI. In order to classify the unbalanced dataset in the ERP-based BCI, an improvement was made to the current binary support vector machine (SVM). Also, based on the expectation of the decision time and accuracies, two adaptive decision criteria were proposed, which was shown to effectively improve the performance of the online BCI. What is more, an algorithm based on one-class SVM was proposed in this study for asynchronous BCI. By generalizing a previous probability model, our approach provides an promising way to estimate and detect the‘idle state’within a statistical framework.
Furthermore, the first online BCI system based on mVEP was presented in this study. Using the EEG signal recorded from only a single channel, an average information transfer rate of 42.1 bits/min was achieved among 12 subjects. In addition, an online application for web searching and browsing was developed based on this paradigm. The promising results, that all of the 12 subjects were able to operate the system with high performance, validate the feasibility of a practical mVEP-based BCI, which could be embedded in screen elements and accommodate diverse interfaces, to provide a better human-computer interaction with higher efficiency and more friendly interface.
Moreover, by employing the independent component analysis, this study analyzed the cognitive component of selective attention under mVEP setting. The preliminary results suggest the feasibility of an independent BCI based on mVEP.
运动起始视觉诱发电位(motion onset visual evoked potential,mVEP)近年来首次被本实验室引入到脑-机接口的研究中。在此基础上,本文系统地研究了mVEP的生理特性,比如不应期效应,刺激模式以及对比度对基于mVEP的脑-机接口系统的影响,为系统设计提供了基础。论文还针对N200成分在不同对比度下的稳定性提出了相应的导联选择方案,使得基于mVEP的脑-机接口系统在低对比度下也能获得稳定的性能。
脑电信号的特征提取和模式分类是脑-机接口系统的核心。本文对现有二分类的支持向量机(SVM)做出了改进,使其能够更好地实现对脑-机接口中不平衡样本的分类。本文中还提出了基于判决时间期望和基于判决准确率期望的两种自适应决策准则,有效地提高了在线脑-机接口的性能。本文提出了基于一类SVM的的异步脑-机接口算法,并通过对后验概率模型的推广,实现了对“空闲状态”的概率估计和检测。
基于论文中的研究和算法,我们开发出了首个基于mVEP的在线单导联的脑-机接口。12位受试者使用该系统的平均信息传输率达到42.1比特/分钟。此外,我们还在此基础上开发了一套网页浏览和搜索系统。实验中所有12位受试者都能够实现对该系统自如的操作,证明了该系统能够应用于实际的场景,嵌入并适应不同的界面,最终实现高效率的脑机交互。
最后,我们采用独立分量分析深入分析了选择性注意下的mVEP成分,并验证了基于mVEP的独立脑-机接口的可行性。
Brain-computer interface (BCI) is a newly developed approach of the multi-modal human-computer interaction. Different from the previous human-computer interaction, BCI could provide a non-muscular output, which allows direct interaction between human brain and the outside environment. Although the proof-of-concept of BCI systems was implemented decades ago, several major challenges are still to be addressed when moving towards a practical online BCI system, such as ease of the system setup, the adaptability of an online system, and the design of the user interface when presented in a real-world application.
Motion onset visual evoked potential (mVEP) has been introduced into BCI research recently by our group. Based on our previous studies, this paper intensively studied the properties that could affect the performance of the mVEP-based BCI, such as the refractory effect, the stimulus pattern, and the background contrast. Specifically, based on the robust N200 component in varied contrast, and the corresponding strategy of channel selection, the proposed mVEP-based BCI system could achieve a stable accuracy even in a low contrast.
Feature extraction and classification of the EEG are critical factors in the online BCI. In order to classify the unbalanced dataset in the ERP-based BCI, an improvement was made to the current binary support vector machine (SVM). Also, based on the expectation of the decision time and accuracies, two adaptive decision criteria were proposed, which was shown to effectively improve the performance of the online BCI. What is more, an algorithm based on one-class SVM was proposed in this study for asynchronous BCI. By generalizing a previous probability model, our approach provides an promising way to estimate and detect the‘idle state’within a statistical framework.
Furthermore, the first online BCI system based on mVEP was presented in this study. Using the EEG signal recorded from only a single channel, an average information transfer rate of 42.1 bits/min was achieved among 12 subjects. In addition, an online application for web searching and browsing was developed based on this paradigm. The promising results, that all of the 12 subjects were able to operate the system with high performance, validate the feasibility of a practical mVEP-based BCI, which could be embedded in screen elements and accommodate diverse interfaces, to provide a better human-computer interaction with higher efficiency and more friendly interface.
Moreover, by employing the independent component analysis, this study analyzed the cognitive component of selective attention under mVEP setting. The preliminary results suggest the feasibility of an independent BCI based on mVEP.
引文
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[3] Allison B, Wolpaw E, Wolpaw J. Braincomputer interface systems: progress and prospects. Expert review of medical devices, 2007, 4(4): 463-474
[4] Celesia G, Peachey N. Visual evoked potentials and electroretinograms. Electroencephalography Basic principles, clinical applications, and related fields, 1993, 911–936
[5] Vidal J. Toward direct brain-computer communication. Annual review of Biophysics and Bioengineering, 1973, 2(1): 157-180
[6] Vidal J. Real-time detection of brain events in EEG. Proceedings of the IEEE, 1977, 65(5): 633-641
[7] Lee P, Hsieh J, Wu C, et al. Brain computer interface using flash onset and offset visual evoked potentials. Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology, 2008, 119(3): 605
[8] Middendorf M, McMillan G, Calhoun G, et al. Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Transactions on Rehabilitation Engineering, 2000, 8(2): 211-214
[9] Cheng M, Gao X, Gao S, et al. Design and implementation of a brain-computer interface with high transfer rates. IEEE Transactions on Biomedical Engineering, 2002, 49(10): 1181-1186
[10]谢勤岚,杨仲乐,陈亚光.基于模拟事件相关电位的脑—计算机接口.计算机工程与应用, 2003, 39(020): 119-121
[11] Martens S, Hill N, Farquhar J, et al. Overlap and refractory effects in a brain–computer interface speller based on the visual P300 event-related potential. Journal of Neural Engineering, 2009, 6(2): 026003
[12] Guo F, Hong B, Gao X, et al. A brain--computer interface using motion-onset visual evoked potential. Journal of Neural Engineering, 2008, 5(4): 477-485
[13] Hong B, Guo F, Liu T, et al. N200-speller using motion-onset visual response. Clinical Neurophysiology, 2009, 120(9): 1658-1666
[14] Heinrich S. A primer on motion visual evoked potentials. Documenta Ophthalmologica, 2007, 114(2): 83-105
[15] Kuba M, KubováZ. Visual evoked potentials specific for motion onset. Documenta Ophthalmologica, 1992, 80(1): 83-89
[16] Kuba M, KubováZ, Kremlá?ek J, et al. Motion-onset VEPs: characteristics, methods, and diagnostic use. Vision Research, 2007, 47(2): 189-202
[17] G pfert E, Müller R, Simon E. The human motion onset VEP as a function of stimulation area for foveal and peripheral vision. Documenta Ophthalmologica, 1990, 75(2): 165-173
[18] Schellart N, Trindade M, Reits D, et al. Temporal and spatial congruence of components of motion-onset evoked responses investigated by whole-head magneto-electroencephalography. Vision Research, 2004, 44(2): 119-134
[19] Hollants-Gilhuijs M, De Munck J, Kubova Z, et al. The development of hemispheric asymmetry in human motion VEPs. Vision Research, 2000, 40(1): 1-11
[20] Mangun G, Hillyard S. 3 Mechanisms and models of selective attention. Electrophysiology of mind: Event-related brain potentials and cognition, 1996, 40
[21] Mangun G. Neural mechanisms of visual selective attention. Psychophysiology, 1995, 32(1): 4-18
[22] Mason S, Bashashati A, Fatourechi M, et al. A comprehensive survey of brain interface technology designs. Annals of Biomedical Engineering, 2007, 35(2): 137-169
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