基于多类运动感知脑电的异步脑—机接口的研究
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摘要
脑-机接口(brain-computer interface, BCI)是一种不依赖于大脑外周神经与肌肉正常输出通道的通讯控制系统,是在大脑与外部设备之间建立的直接交流通道。基于脑电的BCI技术是脑神经科学,认知科学,康复工程,现代信息学与计算机科学等学科共同发展的产物,实现了人脑意识信息与计算机等电子设备间的交换,对进一步研究人的思维机理,帮助有行为障碍的人康复都具有十分重要的作用。对脑-机接口的研究具有非常重要的科学意义、学术价值和广阔的应用前景,是当今世界研究的热点。
基于想象运动的脑电信号(electroencephalogram, EEG)不需要结构化的物理环境,是一种被广泛采用的思维作业方式。脑-机接口技术从实验室研究走向实际应用的关键在于对运动想象脑电信号的识别精度和识别速度问题。本文在总结前人工作的基础上,对脑电信号的采集、处理等问题进行研究。主要包括以下几个方面：
第一、进行系统信号采集模块设计。在实验室已有的脑电采集设备和信号采集软件基础上,根据本人的课题研究,设计相应的大脑想象运动任务实验。根据要求,使用VC++6.0编写出相应的脑电信号采集软件。
第二、对采集的脑电信号进行预处理。由于脑电信号非常微弱,在采集过程中容易受到各种噪声干扰的影响,对脑电信号必须进行去噪处理。本文主要通过小波变化、数字滤波等方法对采集到的信号进行处理,把信号中的无用成分包括心电、眼电、肌电等伪迹去除掉,提取其中的有效信息为后面的处理做准备。
第三、对脑电信号进行特征提取。特征提取的目的是将从预处理后获得的EEG信号变换为能代表不同意识任务的特征向量。能否提取出大脑思维活动中的有效特征信息是BCI研究的关键所在,是正确识别不同思维意识模式的基础。本文主要讨论自适应回归(Adaptive Autoregressive,AAR)模型、独立分量分析(Independent Component Analysis, ICA)、共用空间模式(Common Spatial Pattern,CSP)几种典型的特征提取方法,并进行比较。
第四、对脑电特征进行分类识别。采用何种分类设计是BCI系统中另一十分重要的任务,直接影响到BCI系统的性能。本文主要对Fisher线性判别法、神经网络、支持向量机(Support Vector Machine, SVM)几种分类方法进行探讨。
Brain-computer interface is a direct communication channel which is established between the cerebrum and external devices, and is a communication control system that is not dependent on normal output channels made up of cerebral ganglion and muscle. EEG-based BCI technology is a discipline which is formed by brain science, cognitive science, rehabilitation engineering, modern information science and computer science.It achieves an exchange between the human brain information and computer or other electronic equipments. It can provide a new way of communication and control for paralysis patients, especially who lost the basic physical movements but thinking. So, it is being received increasing attention, and it also has more important scientific significance and academic value.
EEG-based Imagine movement is a widely used way of thinking operations which does not require structured physical environment. EEG recognition accuracy and recognition speed of the motor imagery is the key to successful application of Brain computer interface technology. This paper summarized the basis of previous work, researched the acquisition and processing of the EEG signals.
First, design the signal acquisition module for system. The paper designs imaginary-movement-task experiments. It compiles EEG acquisition software by using VC ++6.0.
Second, pretreat EEG acquisition. As the EEG signal is very weak, it must be carried out on EEG denoising in the collection process. For example, we used the wavelet transform, digital filtering method to collecte signals.
Third, EEG feature extraction. The purpose of feature extraction is to transform the obtained pretreatment EEG signal into different feature vector. Weather can we extract effective information in the brain activity of thinking characteristic,is the key to BCI research and the key to identify the correct sense of the basis for the different ways of thinking. This paper discusses and compares the Adaptive Autoregressive (AAR) model, Independent Component Analysis(ICA), Common Spatial Pattern(CSP) and several typical feature extraction method.
Fourth, EEG classification. Classification for EEG designs is directly affecting the performance of BCI system. This article probes some classification methods, such as the Fisher linear discriminance, Neural networks, Support Vector Machine(SVM), and so on.
基于想象运动的脑电信号(electroencephalogram, EEG)不需要结构化的物理环境,是一种被广泛采用的思维作业方式。脑-机接口技术从实验室研究走向实际应用的关键在于对运动想象脑电信号的识别精度和识别速度问题。本文在总结前人工作的基础上,对脑电信号的采集、处理等问题进行研究。主要包括以下几个方面：
第一、进行系统信号采集模块设计。在实验室已有的脑电采集设备和信号采集软件基础上,根据本人的课题研究,设计相应的大脑想象运动任务实验。根据要求,使用VC++6.0编写出相应的脑电信号采集软件。
第二、对采集的脑电信号进行预处理。由于脑电信号非常微弱,在采集过程中容易受到各种噪声干扰的影响,对脑电信号必须进行去噪处理。本文主要通过小波变化、数字滤波等方法对采集到的信号进行处理,把信号中的无用成分包括心电、眼电、肌电等伪迹去除掉,提取其中的有效信息为后面的处理做准备。
第三、对脑电信号进行特征提取。特征提取的目的是将从预处理后获得的EEG信号变换为能代表不同意识任务的特征向量。能否提取出大脑思维活动中的有效特征信息是BCI研究的关键所在,是正确识别不同思维意识模式的基础。本文主要讨论自适应回归(Adaptive Autoregressive,AAR)模型、独立分量分析(Independent Component Analysis, ICA)、共用空间模式(Common Spatial Pattern,CSP)几种典型的特征提取方法,并进行比较。
第四、对脑电特征进行分类识别。采用何种分类设计是BCI系统中另一十分重要的任务,直接影响到BCI系统的性能。本文主要对Fisher线性判别法、神经网络、支持向量机(Support Vector Machine, SVM)几种分类方法进行探讨。
Brain-computer interface is a direct communication channel which is established between the cerebrum and external devices, and is a communication control system that is not dependent on normal output channels made up of cerebral ganglion and muscle. EEG-based BCI technology is a discipline which is formed by brain science, cognitive science, rehabilitation engineering, modern information science and computer science.It achieves an exchange between the human brain information and computer or other electronic equipments. It can provide a new way of communication and control for paralysis patients, especially who lost the basic physical movements but thinking. So, it is being received increasing attention, and it also has more important scientific significance and academic value.
EEG-based Imagine movement is a widely used way of thinking operations which does not require structured physical environment. EEG recognition accuracy and recognition speed of the motor imagery is the key to successful application of Brain computer interface technology. This paper summarized the basis of previous work, researched the acquisition and processing of the EEG signals.
First, design the signal acquisition module for system. The paper designs imaginary-movement-task experiments. It compiles EEG acquisition software by using VC ++6.0.
Second, pretreat EEG acquisition. As the EEG signal is very weak, it must be carried out on EEG denoising in the collection process. For example, we used the wavelet transform, digital filtering method to collecte signals.
Third, EEG feature extraction. The purpose of feature extraction is to transform the obtained pretreatment EEG signal into different feature vector. Weather can we extract effective information in the brain activity of thinking characteristic,is the key to BCI research and the key to identify the correct sense of the basis for the different ways of thinking. This paper discusses and compares the Adaptive Autoregressive (AAR) model, Independent Component Analysis(ICA), Common Spatial Pattern(CSP) and several typical feature extraction method.
Fourth, EEG classification. Classification for EEG designs is directly affecting the performance of BCI system. This article probes some classification methods, such as the Fisher linear discriminance, Neural networks, Support Vector Machine(SVM), and so on.
引文
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[2]Obermeier B, Guger C, Neuper C and Pfurtscheller G, Hidden Markov models for online classification of single trial, EEG Pattern Recognit. Lett,2001, (1):299～309.
[3]Lee H and Choi S, PCA+HMM+SVM for EEG pattern classification, Proc.7th Int. Symp. on Signal Processing and its Applications,2003.
[4]Garcia G N, Ebrahimi T and Vesin J-M, Support vector EEG classification in the Fourier and time-frequency correlation domains, Conference Proc.1st Int. IEEE EMBS Conf. on Neural Engineering,2003.
[5]Boostani R and Moradi M H, A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier, J. Neural Eng.,2004, (1):212～217.
[6]Solhjoo S and Moradi M H, Mental task recognition:a comparison between some of classification methods, BIOSIGNAL 2004 Int. EURASIP Conference,2004.
[7]Lemm S, Schafer C and Curio G, BCI competition 2003-data set III:probabilistic modeling of sensorimotor mu rhythms for classification of imaginary hand movements, IEEE Trans. Biomed. Eng.2004,(51):1077～1080.
[8]Solhjoo S, Nasrabadi A M and Golpayegani M R H, Classification of chaotic signals using HMM classifiers:EEG-based mental task classification, Proc. European Signal Processing Conference,2005.
[9]Tavakolian K, Vasefi F, Naziripour K and Rezaei S, Mental task classification for brain computer interface applications Proc. Canadian Student Conf. on Biomedical Computing,2006.
[10]Cincotti F, Scipione A, Tiniperi A, Mattia D, Marciani M G, Millan J del R, Salinari S, Bianchi L and Babiloni F, Comparison of different feature classifiers for brain computer interfaces, Proc.1st Int. IEEE EMBS Conf. on Neural Engineering,2003.
[11]Townsend G, Graimann B, Pfurtscheller G, Continuous EEG classification during motor imagery-simulation of an asynchronous BCI, IEEE Trans Neural Syst Rehabil Eng,12(2): 258～265.
[12]Tsui Chun Sing Louis, Gan John Q, Asyncronous BCI control of a robot simulator with supervised online training, IDEAL,2007:125～134.
[13]Kalcher J, Flotzinger D, Neuper C, Golly S and Pfurtscheller G, Graz brain-computer interface Ⅱ:towards communication between humans and computers based on online classification of three different EEG patterns, Med. Biol. Eng.Comput,1996,(34):383～88.
[14]Obermaier B, Neuper C, Guger C and Pfurtscheller G, Information transfer rate in a five-classes brain-computer interface, IEEE Trans. Neural Syst. Rehabil.Eng.,2000,(9):283～ 288.
[15]Yijun Wang, Bo Hong, Xiaorong Gao, and Shangkai Gao, Implementation of a Brain-Computer Interface Based on Three States of Motor Imagery, Proceedings of the 29th Annual International, Conference of the IEEE EMBS,2007.
[16]Alois Schlogl, Felix Lee, Horst Bischof and Gert Pfurtscheller, Characterization of four-class motor imagery EEG data for the BCI-competition 2005,J. Neural Eng.,2005, (2):L14-L22.
[17]Naeem, M., Brunner, C., Leeb, R., Graimann, B., Pfurtscheller, G., Separability of four-class motor imagery data using independent components analysis, J. Neural Eng.,2006, (3):208～216.
[18]Pfurtscheller G., Brunner, C., Schlogl, A., Lopes da Silva, F.H., Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage,2006,(31),153-159.
[19]Clemens Brunner, Muhammad Naeem, Robert Leeb, Bernhard Graimann, Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis, Pattern Recognition Letters,28,2007:957～964.
[20]Valerie Morash, Ou Bai, Stephen Furlani, Peter Lin,Classifying EEG signals preceding right hand, left hand, tongue, and right foot movements and motor imageries, Clinical Neurophysiology,2008, (119):2570-2578.
[21]Bai Ou, Lin Peter et al, A high performance sensorimotor Beta rhythm-based brain-computer interface associated with human natural motor behavior, Journal of Neural Engineering,2008:24～35.
[22]lou Bin, Hong Bo, Gao Xiaorong, Gao Shangkai, Bipolar electrode selection for a motor imagery based brain-computer interface, Journal of Neural Engineering,2008, (5):342～ 349.
[23]Tsui Chun Sing Louis, Gan John Q, Stephen J. Roberts, A self-paced brain-computer interface for controlling a robot simulator:an online event labelling paradigm and an extended Kalman filter based algorithm for online training, Med Biol Eng Comput,2009, (47):257-265.
[24]G. Pfurtscheller, C. Neuper, C. Guger, W. Harkam, H. Ramoser, A.Schlogl, B. Obermaier, and M. Pregenzer, Current trends in graz brain-computer-interface (BCI) research, IEEE Trans. Rehab. Eng.,2000, (8):216-219.
[25]F Lotte, M Congedo, A Lecuyer, F Lamarche and B Arnaldi, A review of classification algorithms for EEG-based brain-computer interfaces, Journal of Neural Engineering,2007,4, R1-R13.
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