左右手运动想象脑电采集和特征提取方法初探
摘要
脑机接口(BCI)技术给肢体运动残疾人带来了康复的希望,基于脑电的BCI系统更是因为其结构简单,信号采集容易以及方便携带和无损成为研究的热点。
本文首先介绍了脑电信号的特征、脑机接口的概念和目前国内外的研究情况,在参考国外BCI实验范式的基础上对左右手运动想象的脑电采集的方法进行了探索,利用Presentation编写了运动想象提示程序“Visual Guide”,训练被试,并成功采集到左右手运动想象的脑电特征信号,成功采集率83%。
为了实现运动想象意识信号特征的有效提取,我们对采集到的脑电信号进行了信号的预处理,并对信号进行了频域分析、时域分析、时频联合分析,依据时频信号的分析结果利用小波对特征信号进行了有效的提取。通过频域数据分析我们发现了C3和C4处脑电信号能量在10Hz附近有最大的区分,通过时域分析证明10Hz的C3和C4电极信号确实与左右手运动想象提示相关。时频联合分析进一步证明了此结论。根据这些时频域特征,我们用Morlet小波对原始脑电信号进行分解,成功提取了特征信号。
The advancement of Brain-Computer interface (BCI) study brings hopes of rehabilitation to the handicapped. And the BCI based on electroencephalograph has been a hotspot for the BCI study, because of its simple structure, safety and convenience of signal acquisition and portage.
In this thesis, the characters of the Electroencephalogram (EEG) signal, the concept of the BCI , the domestic and international research status of BCI were introduced first. Then the paradigm of our own was groped for after the paradigm of the movement imagination studies of the groups oversea being studied, and the program‘Visual Guide’for visual cues of the hands movement imagination was made. Testees were trained with this program and the signal with enough distinction was acquired finally with the successful rate of 83%.
For the goal that features of the signal would be extracted effectively, the signal acquired previously was pretreated, and then the signal was analyzed in frequency domain , time domain and time-frequency domain. Through frequency domain analysis, the signal got from C3 and C4 had greatest distinction about the 10Hz, and the signal of 10Hz had correlation with the cues of left hand and right hand movement imagination through time domain analysis. And the time-frequency analysis had further sustained the conclusion. With the features that we got from the time-frequency domain analysis, the original signal was decomposed and the signal with the features was extracted successfully through the Morlet wavelet transformation.
本文首先介绍了脑电信号的特征、脑机接口的概念和目前国内外的研究情况,在参考国外BCI实验范式的基础上对左右手运动想象的脑电采集的方法进行了探索,利用Presentation编写了运动想象提示程序“Visual Guide”,训练被试,并成功采集到左右手运动想象的脑电特征信号,成功采集率83%。
为了实现运动想象意识信号特征的有效提取,我们对采集到的脑电信号进行了信号的预处理,并对信号进行了频域分析、时域分析、时频联合分析,依据时频信号的分析结果利用小波对特征信号进行了有效的提取。通过频域数据分析我们发现了C3和C4处脑电信号能量在10Hz附近有最大的区分,通过时域分析证明10Hz的C3和C4电极信号确实与左右手运动想象提示相关。时频联合分析进一步证明了此结论。根据这些时频域特征,我们用Morlet小波对原始脑电信号进行分解,成功提取了特征信号。
The advancement of Brain-Computer interface (BCI) study brings hopes of rehabilitation to the handicapped. And the BCI based on electroencephalograph has been a hotspot for the BCI study, because of its simple structure, safety and convenience of signal acquisition and portage.
In this thesis, the characters of the Electroencephalogram (EEG) signal, the concept of the BCI , the domestic and international research status of BCI were introduced first. Then the paradigm of our own was groped for after the paradigm of the movement imagination studies of the groups oversea being studied, and the program‘Visual Guide’for visual cues of the hands movement imagination was made. Testees were trained with this program and the signal with enough distinction was acquired finally with the successful rate of 83%.
For the goal that features of the signal would be extracted effectively, the signal acquired previously was pretreated, and then the signal was analyzed in frequency domain , time domain and time-frequency domain. Through frequency domain analysis, the signal got from C3 and C4 had greatest distinction about the 10Hz, and the signal of 10Hz had correlation with the cues of left hand and right hand movement imagination through time domain analysis. And the time-frequency analysis had further sustained the conclusion. With the features that we got from the time-frequency domain analysis, the original signal was decomposed and the signal with the features was extracted successfully through the Morlet wavelet transformation.
引文
[1]李继硕.神经解剖学.第一版.西安:第四军医大学出版社, 1998: 143~145
[2] http: //www. drdavidlewis. co. uk/default. aspx?ID=3
[3] http: //zhidao. baidu. com/question/23707399. html?si=3
[4]陈兴时,张明岛.脑电学发展史实.国外医学精神病学分册, 1996, 23(2): 95~98
[5]张唯真.生物医学电子学.第一版.北京:清华大学出版社, 1990: 16~17
[6] Hochberg L R, Serruya M D, Friehs G M et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature. 2006, 442(13): 164~170
[7]张唯真.生物医学电子学.第一版.北京:清华大学出版社, 1990. 19~20
[8]杨帮国,颜国正,丁国清.脑机接口关键技术研究.北京生物医学工程, 2005, 24(4): 308~310
[9] Lebedev M A, Nicolelis M A. L. Brain-Machine interfaces: past, present and future. TRENDs in Neurosciences, 2006, 29(9): 536~543
[10] Sitaram R, Zhang H H, Guan C T. Temporal classification of multi-channel near- infrared spectroscopy signals of motor imagery for developing a brain–computer interface. NeuroImage, 2007, 34: 1416~1427
[11]高上凯.浅谈脑—机接口的发展现状与挑战.中国生物医学工程学报, 2007, 26(6): 801~803
[12] Mason S G, Bashashati A, Fatoureehi M, et al. A comprehensive survey of brain interface technology designs. Ann Biomed Eng, 2007, 35: 137~l69
[13]程明,高上凯,张琳.基于脑电信号的脑-计算机接口.北京生物医学工程, 2000, 19(2): 113~117
[14] Wang Y J, Wang R P, Gao X R, Hong B, Gao S. K, A practical VEP-based brain- computer interface, IEEE Trans. on Neural System and Rehabilitation Engineering, 2006, 14(2): 1~2
[15]马赘,王毅军,高小榕.基于脑机接口技术的虚拟现实康复训练平台.中国生物医学工程学报, 2007, 26(3): 373~378
[16]程明,任宇鹏,高小榕等.脑电控制康复机器人的关键技术.机器人技术与应用, 2003, 2(4): 45~48
[17]赵丽,万柏坤.基于P300的脑机接口系统研究.天津工程师范学院学报, 2005, 15(2): 5~9
[18]万柏坤,綦宏志,赵丽.基于脑电Alpha波的脑-机接口控制实验.天津大学学报, 2006, 39(8): 978~984
[19]张谦,王振,秦琦等.基于脑电Alpha波的脑一机接口控制系统研究.医疗卫生装备, 2006, 27(6): 30~32
[20]周鹏,曹红宝,熊屹等.基于脑机接口的智能康复系统的设计.计算机工程与应用, 2007, 43(26): 1~4
[21]官金安,陈亚光,黄敏.单通道脑电信号中诱发电位的单次提取.生物医学工程学杂志, 2006, 23(2): 252~256
[22]官金安,陈亚光.脑控双页虚拟键盘的设计与性能分析.中国临床康复, 2006, 10(9): 124~126
[23]何庆华,彭承琳,吴宝明.基于视觉诱发电位的脑机接口实验研究.生物医学工程学杂志, 2004, 21(1): 93~96
[24]何庆华,吴宝明,彭承琳.基于小波和神经网络的视觉诱发电位识别方法.仪器仪表学报, 2007, 28(6): 1003~1006
[25]王禾,何庆华,吴宝明等.视觉诱发电位判别注视目标的实验.中国临床康复, 2005, 9(16): 82~83
[26] Farwell L A, Donchin E. Talking of the top of your head:toward a mental prosthesis utilizing event-related brain potentials[J].Electroenceph Clin Neurophysiol, 1988, 70: 510~523
[27]杨帮华.自发脑电脑机接口技术及脑电信号识别方法研究: [博士学位论文].上海交通大学图书馆, 2006
[28] Wolpaw J R, Birbaumer N, McFarland D. J et al. Brain–computer interfaces for communication and control. Clinical Neurophysiology, 2002, 113: 767~791
[29]第二次全国残疾人抽样调查领导小组. 2006年第二次全国残疾人抽样调查主要数据公报, 2006
[30] Pfurtscheller G, Neuper C. Motor Imagery and Direct Brain–Computer Communica -tion. Proceedings of The IEEE, 2001, 89(7): 1123~1126
[31]魏景汉,罗跃嘉.认知事件相关脑电位教程.北京:经济日报出版社, 2000: 336~356
[32]赵仑. ERP实验教程.第一版.天津:天津社会科学院出版社, 2004: 40~41
[33] http: //ida. first. fraunhofer. de/projects/bci/competition_iii/
[34] Katsuhiro I, Ryo K, Tomoyula N et al. EEG Signal Analysis based on Quasi-AR Model. SICE Annual Conference in Sapporo, August 4-6, 2004: 2197~2201
[35] Pei X M, Zheng C X. feature extraction and classification of brain motor imagery task based on MVAR model. Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, 2004, 8: 26~29
[36] Pfurtscheller G, Lopes da Silva F H. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology, 1999, 110: 1842~1857
[37] Kalcher J, Pfurtscheller G. Discrimination between phase-locked and non- phase-locked event-related EEG activity, Electroencephalography and clinical Neurophysiology 1995, 94: 381~384
[38]金晶,王行愚,张秀.基于能量特征的左右手运动想象脑信号的识别方法.华东理工大学学报(自然科学版), 2007, 8(133): 536~540
[39]薛年喜. Matlab在数字信号处理中的应用.北京:清华大学出版社, 2003: 252~280
[40]黄文梅,熊桂林,杨勇.信号分析与处理——Matlab语言与应用.长沙:国防科技大学出版社. 2000
[41]徐宝国,宋爱国.单次运动想象脑电的特征提取和分类.东南大学学报(自然科学版), 2007, 37(4): 629~633
[42]诸强.左右手运动想象脑电模式的识别.生物医学工程学杂志, 2004, 21 (6): 1031~1034
[43] Edlinger G, Prull A, Neuper C, Pfurtscheller G. High-Resolution ERD: Cortical ima-ging of event-related desynchronization during motor imagery. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1998, 20(4): 2128~2130
[44] Pfurtscheller G, Neuper C. Motor imagery actives primary sensorimotor area in humans. Neuroscience letters, 1997, 239: 65~68
[45] http: //tftb. nongnu. org/
[46]葛哲学,陈仲生, Matlab时频分析技术及应用.第一版.北京:人民邮电出版社, 2006. 1. 152~159
[47]胡昌华,李国华,刘涛等,基于Matlab 6. x的系统分析与设计——小波分析.第二版.西安:西安电子科技大学出版社, 2004. 1. 1
[48]徐伯勋,白旭滨,傅孝毅.信号处理中的数学变换和估计方法.第一版.北京:清华大学出版社, 2004. 7: 2~4
[49]胡昌华,周涛,夏启兵等.基于Matlab的系统分析与设计——时频分析.第一版.西安:西安电子科技大学出版社, 2002. 7: 1~2
[50]廖祥,尹愚,尧德中等.基于支持向量机框架的运动想象脑电分类.中国科技论文在线, 2005. http: //www. paper. edu. cn/paper. php?serial_number=200512-184
[51] Pfurtscheller G, Kalcher J, Neuper C et al. On-line EEG classification during externally-paced hand movements. Electroencephalography and clinical Neurophysiology, 1996, 99: 416~425
[52] Pfurtscheller G, Neupera C, Flotzinger D. EEG-based discrimination between imagination of right and left hand movement. Electroencephalography and clinical Neurophysiology, 1997(103): 642-651
[53] Zhu X Y, Wu J K, Cheng Y M. A Unified Framework to Exploit Information in BCI Data for Continuous Prediction. Neurocomputing, 2008, 71: 1022-1031
[54]李钢,王蔚,张胜.支持向量机在脑电信号分类中的应用.计算机应用, 2006, 26(6): 1431~1433
[2] http: //www. drdavidlewis. co. uk/default. aspx?ID=3
[3] http: //zhidao. baidu. com/question/23707399. html?si=3
[4]陈兴时,张明岛.脑电学发展史实.国外医学精神病学分册, 1996, 23(2): 95~98
[5]张唯真.生物医学电子学.第一版.北京:清华大学出版社, 1990: 16~17
[6] Hochberg L R, Serruya M D, Friehs G M et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature. 2006, 442(13): 164~170
[7]张唯真.生物医学电子学.第一版.北京:清华大学出版社, 1990. 19~20
[8]杨帮国,颜国正,丁国清.脑机接口关键技术研究.北京生物医学工程, 2005, 24(4): 308~310
[9] Lebedev M A, Nicolelis M A. L. Brain-Machine interfaces: past, present and future. TRENDs in Neurosciences, 2006, 29(9): 536~543
[10] Sitaram R, Zhang H H, Guan C T. Temporal classification of multi-channel near- infrared spectroscopy signals of motor imagery for developing a brain–computer interface. NeuroImage, 2007, 34: 1416~1427
[11]高上凯.浅谈脑—机接口的发展现状与挑战.中国生物医学工程学报, 2007, 26(6): 801~803
[12] Mason S G, Bashashati A, Fatoureehi M, et al. A comprehensive survey of brain interface technology designs. Ann Biomed Eng, 2007, 35: 137~l69
[13]程明,高上凯,张琳.基于脑电信号的脑-计算机接口.北京生物医学工程, 2000, 19(2): 113~117
[14] Wang Y J, Wang R P, Gao X R, Hong B, Gao S. K, A practical VEP-based brain- computer interface, IEEE Trans. on Neural System and Rehabilitation Engineering, 2006, 14(2): 1~2
[15]马赘,王毅军,高小榕.基于脑机接口技术的虚拟现实康复训练平台.中国生物医学工程学报, 2007, 26(3): 373~378
[16]程明,任宇鹏,高小榕等.脑电控制康复机器人的关键技术.机器人技术与应用, 2003, 2(4): 45~48
[17]赵丽,万柏坤.基于P300的脑机接口系统研究.天津工程师范学院学报, 2005, 15(2): 5~9
[18]万柏坤,綦宏志,赵丽.基于脑电Alpha波的脑-机接口控制实验.天津大学学报, 2006, 39(8): 978~984
[19]张谦,王振,秦琦等.基于脑电Alpha波的脑一机接口控制系统研究.医疗卫生装备, 2006, 27(6): 30~32
[20]周鹏,曹红宝,熊屹等.基于脑机接口的智能康复系统的设计.计算机工程与应用, 2007, 43(26): 1~4
[21]官金安,陈亚光,黄敏.单通道脑电信号中诱发电位的单次提取.生物医学工程学杂志, 2006, 23(2): 252~256
[22]官金安,陈亚光.脑控双页虚拟键盘的设计与性能分析.中国临床康复, 2006, 10(9): 124~126
[23]何庆华,彭承琳,吴宝明.基于视觉诱发电位的脑机接口实验研究.生物医学工程学杂志, 2004, 21(1): 93~96
[24]何庆华,吴宝明,彭承琳.基于小波和神经网络的视觉诱发电位识别方法.仪器仪表学报, 2007, 28(6): 1003~1006
[25]王禾,何庆华,吴宝明等.视觉诱发电位判别注视目标的实验.中国临床康复, 2005, 9(16): 82~83
[26] Farwell L A, Donchin E. Talking of the top of your head:toward a mental prosthesis utilizing event-related brain potentials[J].Electroenceph Clin Neurophysiol, 1988, 70: 510~523
[27]杨帮华.自发脑电脑机接口技术及脑电信号识别方法研究: [博士学位论文].上海交通大学图书馆, 2006
[28] Wolpaw J R, Birbaumer N, McFarland D. J et al. Brain–computer interfaces for communication and control. Clinical Neurophysiology, 2002, 113: 767~791
[29]第二次全国残疾人抽样调查领导小组. 2006年第二次全国残疾人抽样调查主要数据公报, 2006
[30] Pfurtscheller G, Neuper C. Motor Imagery and Direct Brain–Computer Communica -tion. Proceedings of The IEEE, 2001, 89(7): 1123~1126
[31]魏景汉,罗跃嘉.认知事件相关脑电位教程.北京:经济日报出版社, 2000: 336~356
[32]赵仑. ERP实验教程.第一版.天津:天津社会科学院出版社, 2004: 40~41
[33] http: //ida. first. fraunhofer. de/projects/bci/competition_iii/
[34] Katsuhiro I, Ryo K, Tomoyula N et al. EEG Signal Analysis based on Quasi-AR Model. SICE Annual Conference in Sapporo, August 4-6, 2004: 2197~2201
[35] Pei X M, Zheng C X. feature extraction and classification of brain motor imagery task based on MVAR model. Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, 2004, 8: 26~29
[36] Pfurtscheller G, Lopes da Silva F H. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology, 1999, 110: 1842~1857
[37] Kalcher J, Pfurtscheller G. Discrimination between phase-locked and non- phase-locked event-related EEG activity, Electroencephalography and clinical Neurophysiology 1995, 94: 381~384
[38]金晶,王行愚,张秀.基于能量特征的左右手运动想象脑信号的识别方法.华东理工大学学报(自然科学版), 2007, 8(133): 536~540
[39]薛年喜. Matlab在数字信号处理中的应用.北京:清华大学出版社, 2003: 252~280
[40]黄文梅,熊桂林,杨勇.信号分析与处理——Matlab语言与应用.长沙:国防科技大学出版社. 2000
[41]徐宝国,宋爱国.单次运动想象脑电的特征提取和分类.东南大学学报(自然科学版), 2007, 37(4): 629~633
[42]诸强.左右手运动想象脑电模式的识别.生物医学工程学杂志, 2004, 21 (6): 1031~1034
[43] Edlinger G, Prull A, Neuper C, Pfurtscheller G. High-Resolution ERD: Cortical ima-ging of event-related desynchronization during motor imagery. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1998, 20(4): 2128~2130
[44] Pfurtscheller G, Neuper C. Motor imagery actives primary sensorimotor area in humans. Neuroscience letters, 1997, 239: 65~68
[45] http: //tftb. nongnu. org/
[46]葛哲学,陈仲生, Matlab时频分析技术及应用.第一版.北京:人民邮电出版社, 2006. 1. 152~159
[47]胡昌华,李国华,刘涛等,基于Matlab 6. x的系统分析与设计——小波分析.第二版.西安:西安电子科技大学出版社, 2004. 1. 1
[48]徐伯勋,白旭滨,傅孝毅.信号处理中的数学变换和估计方法.第一版.北京:清华大学出版社, 2004. 7: 2~4
[49]胡昌华,周涛,夏启兵等.基于Matlab的系统分析与设计——时频分析.第一版.西安:西安电子科技大学出版社, 2002. 7: 1~2
[50]廖祥,尹愚,尧德中等.基于支持向量机框架的运动想象脑电分类.中国科技论文在线, 2005. http: //www. paper. edu. cn/paper. php?serial_number=200512-184
[51] Pfurtscheller G, Kalcher J, Neuper C et al. On-line EEG classification during externally-paced hand movements. Electroencephalography and clinical Neurophysiology, 1996, 99: 416~425
[52] Pfurtscheller G, Neupera C, Flotzinger D. EEG-based discrimination between imagination of right and left hand movement. Electroencephalography and clinical Neurophysiology, 1997(103): 642-651
[53] Zhu X Y, Wu J K, Cheng Y M. A Unified Framework to Exploit Information in BCI Data for Continuous Prediction. Neurocomputing, 2008, 71: 1022-1031
[54]李钢,王蔚,张胜.支持向量机在脑电信号分类中的应用.计算机应用, 2006, 26(6): 1431~1433