想象多力度单侧手运动的脑电信号分类研究
|
摘要
目的传统脑机接口实验范式多为左右手运动想象,无力度分级,命令单一,为增加脑机接口命令数,使中风患者在康复期间设计获得更好的治疗方案,设计了想象三种力度下的单侧手运动实验并对其进行分类。方法 9名受试者被要求想分别以三种力度(50%、30%、10%最大自主收缩力)握紧单侧手,同时记录脑电及肌电信号,对脑电信号预处理后进行空间滤波和特征提取,再对处理后的数据进行带通滤波并提取特征,利用线性判别分析作为分类器。结果\结论采用两级特征提取分类方法,平均分类正确率达到72.4%,证明通过分析想象不同力度单侧手运动的脑电信号能够扩展脑机接口命令数。
Aim The traditional brain-computer interface experimental paradigm is mostly for left and right hand movement imagination, no strength distinction, Therefore the order is single, In order to increase the number of brain-computer interface commands, so that stroke patients can get better treatment plans during rehabilitation. a single-handed hand movement experiment with three strengths was designed and classified. Method Nine subjects were asked to imagine clenching their right hands with three different force loads(50% maximum voluntary contraction(MVC), 30%MVC and 10% MVC) and recorded their EEG and EMG signals. After preprocessing the EEG signal, spatial filtering and feature extraction are performed, then the processed data is bandpass filtered and features are extracted, and LDA is used as a classifier. Results\Conclusion Using the two-level feature extraction classification method, the average classification accuracy rate reached 72.4%, which proves that the number of brain-computer interface commands can be extended by analyzing the EEG signals of unilateral hand movements with different strengths.
Aim The traditional brain-computer interface experimental paradigm is mostly for left and right hand movement imagination, no strength distinction, Therefore the order is single, In order to increase the number of brain-computer interface commands, so that stroke patients can get better treatment plans during rehabilitation. a single-handed hand movement experiment with three strengths was designed and classified. Method Nine subjects were asked to imagine clenching their right hands with three different force loads(50% maximum voluntary contraction(MVC), 30%MVC and 10% MVC) and recorded their EEG and EMG signals. After preprocessing the EEG signal, spatial filtering and feature extraction are performed, then the processed data is bandpass filtered and features are extracted, and LDA is used as a classifier. Results\Conclusion Using the two-level feature extraction classification method, the average classification accuracy rate reached 72.4%, which proves that the number of brain-computer interface commands can be extended by analyzing the EEG signals of unilateral hand movements with different strengths.
引文
[1]Birbaumer N,Weber C,Neuper C,et al.Physiological regulation of thinking:brain-computer interface(BCI)research[J].Progress in Brain Research,2006,159(1):369-391.
[2]李明爱,刘净瑜,郝冬梅.基于改进CSP算法的运动想象脑电信号识别方法[J].中国生物医学工程学报,2009,28(2):161-165..
[3]Steenbergen B,CrajéC,Nilsen D M,et al.Motor imagery training in hemiplegic cerebral palsy:a potentially useful therapeutic tool for rehabilitation[J].Developmental Medicine&Child Neurology,2010,51(9):690-696.
[4]王仲朋,陈龙,何峰,等.面向康复与辅助应用的脑-机接口趋势与展望[J].仪器仪表学报,2017,38(6):1307-1318.
[5]Cho W,Sabathiel N,Ortner R,et al.Paired Associative Stimulation Using Brain-Computer Interfaces for Stroke Rehabilitation:A Pilot Study[J].European Journal of Translational Myology,2016,26(3):6132.
[6]Jiang S,Chen L,Wang Z,et al.Application of BCI-FESsystem on stroke rehabilitation[C]//International Ieee/embs Conference on Neural Engineering.IEEE,2015:1112-1115.
[7]Yi W,Qiu S,Wang K,et al.EEG oscillatory patterns and classification of sequential compound limb motor imagery.[J].Journal of Neuroengineering&Rehabilitation,2016,13(1):1-12.
[8]Lafleur K,Cassady K,Doud A,et al.Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface.[J].Journal of Neural Engineering,2013,10(4):046003.
[9]Edelman B J,Baxter B,He B.EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks[J].IEEE Transactions on Biomedical Engineering,2015,63(1):4-14.
[10]Han Y,Perdoni C,He B.Relationship between Speed and EEG Activity during Imagined and Executed Hand Movements[J].Journal of Neural Engineering,2010,7(2):26001.
[11]Nakayashiki K,Saeki M,Takata Y,et al.Modulation of event-related desynchronization during kinematic and kinetic hand movements[J].Journal of Neuroengineering&Rehabilitation,2014,11(1):1-9。
[12]Hoozemans M J,van Die?n J H.Prediction of handgrip forces using surface EMG of forearm muscles.[J].Journal of Electromyography&Kinesiology Official Journal of the International Society of Electrophysiological Kinesiology,2005,15(4):358-366.
[13]Jackson P L,Lafleur M F,Malouin F,et al.Functional cerebral reorganization following motor sequence learning through mental practice with motor imagery.[J].Neuroimage,2003,20(2):1171-1180.
[14]杨帮华,陆文宇,何美燕,等.脑机接口中基于WPD和CSP的特征提取[J].仪器仪表学报,2012,33(11):2560-2565.
[15]Yi W,Qiu S,Qi H,et al.EEG feature comparison and classification of simple and compound limb motor imagery[J].Journal of Neuroengineering&Rehabilitation,2013,10(1):106-106.
[16]Subasi A,Ismail Gursoy M.EEG signal classification using PCA,ICA,LDA and support vector machines[J].Expert Systems with Applications,2010,37(12):8659-8666.
[17]Fu A,Wang C,Qi H,et al.Electromyography-based analysis of human upper limbs during 45-day head-down bed-rest[J].Acta Astronautica,2016,120:260-269.
[2]李明爱,刘净瑜,郝冬梅.基于改进CSP算法的运动想象脑电信号识别方法[J].中国生物医学工程学报,2009,28(2):161-165..
[3]Steenbergen B,CrajéC,Nilsen D M,et al.Motor imagery training in hemiplegic cerebral palsy:a potentially useful therapeutic tool for rehabilitation[J].Developmental Medicine&Child Neurology,2010,51(9):690-696.
[4]王仲朋,陈龙,何峰,等.面向康复与辅助应用的脑-机接口趋势与展望[J].仪器仪表学报,2017,38(6):1307-1318.
[5]Cho W,Sabathiel N,Ortner R,et al.Paired Associative Stimulation Using Brain-Computer Interfaces for Stroke Rehabilitation:A Pilot Study[J].European Journal of Translational Myology,2016,26(3):6132.
[6]Jiang S,Chen L,Wang Z,et al.Application of BCI-FESsystem on stroke rehabilitation[C]//International Ieee/embs Conference on Neural Engineering.IEEE,2015:1112-1115.
[7]Yi W,Qiu S,Wang K,et al.EEG oscillatory patterns and classification of sequential compound limb motor imagery.[J].Journal of Neuroengineering&Rehabilitation,2016,13(1):1-12.
[8]Lafleur K,Cassady K,Doud A,et al.Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface.[J].Journal of Neural Engineering,2013,10(4):046003.
[9]Edelman B J,Baxter B,He B.EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks[J].IEEE Transactions on Biomedical Engineering,2015,63(1):4-14.
[10]Han Y,Perdoni C,He B.Relationship between Speed and EEG Activity during Imagined and Executed Hand Movements[J].Journal of Neural Engineering,2010,7(2):26001.
[11]Nakayashiki K,Saeki M,Takata Y,et al.Modulation of event-related desynchronization during kinematic and kinetic hand movements[J].Journal of Neuroengineering&Rehabilitation,2014,11(1):1-9。
[12]Hoozemans M J,van Die?n J H.Prediction of handgrip forces using surface EMG of forearm muscles.[J].Journal of Electromyography&Kinesiology Official Journal of the International Society of Electrophysiological Kinesiology,2005,15(4):358-366.
[13]Jackson P L,Lafleur M F,Malouin F,et al.Functional cerebral reorganization following motor sequence learning through mental practice with motor imagery.[J].Neuroimage,2003,20(2):1171-1180.
[14]杨帮华,陆文宇,何美燕,等.脑机接口中基于WPD和CSP的特征提取[J].仪器仪表学报,2012,33(11):2560-2565.
[15]Yi W,Qiu S,Qi H,et al.EEG feature comparison and classification of simple and compound limb motor imagery[J].Journal of Neuroengineering&Rehabilitation,2013,10(1):106-106.
[16]Subasi A,Ismail Gursoy M.EEG signal classification using PCA,ICA,LDA and support vector machines[J].Expert Systems with Applications,2010,37(12):8659-8666.
[17]Fu A,Wang C,Qi H,et al.Electromyography-based analysis of human upper limbs during 45-day head-down bed-rest[J].Acta Astronautica,2016,120:260-269.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |