CAJ | 학술논문

为了研究如何从无创运动相关脑电中提取运动信息作为上肢主动康复训练的控制命令，通过设计实验，使右手完成左、上、右3个方向的运动，同时采集脑电数据和右手运动信息。通过小波时频分析确认与右手运动相关的脑电频带，并提取其小波分解系数作为特征，采用支持向量机进行特征分类，根据方向识别准确率分析提取特征的有效性。结果表明，运动脑电 delta 和 theta 频段的小波系数特征可以有效区分右手不同方向的运动，方向识别准确率的均值接近65%，并且用准备阶段特征分类的结果普遍优于运动阶段特征，因此，在手运动之前诱发的脑电活动含有丰富的运动信息，可用于脑-机接口系统提取上肢主动康复训练的控制命令。
위료연구여하종무창운동상관뇌전중제취운동신식작위상지주동강복훈련적공제명령，통과설계실험，사우수완성좌、상、우3개방향적운동，동시채집뇌전수거화우수운동신식。통과소파시빈분석학인여우수운동상관적뇌전빈대，병제취기소파분해계수작위특정，채용지지향량궤진행특정분류，근거방향식별준학솔분석제취특정적유효성。결과표명，운동뇌전 delta 화 theta 빈단적소파계수특정가이유효구분우수불동방향적운동，방향식별준학솔적균치접근65%，병차용준비계단특정분류적결과보편우우운동계단특정，인차，재수운동지전유발적뇌전활동함유봉부적운동신식，가용우뇌-궤접구계통제취상지주동강복훈련적공제명령。
To extract effective motorial information from noninvasive movement-related EEG signal utilized as con-trol commands of active rehabilitation,an experiment was designed which the right hand moved in three directions respectively(left,top,right)and EEG data of reciprocating motion and hand movement trajectory were recorded. Firstly,wavelet analysis was applied to confirm the movement-related bands in frequency domain,and then wavelet decomposition coefficients were extracted as characteristics. Next,the support vector machine algorithm was selected and the effectiveness of feature extraction was estimated through recognition accuracy. The results demonstrate that, the wavelet coefficients of delta and theta bands of movement-related EEG used as characteristics can effectively dis-tinguish right hand movements in different directions and have nice classification accuracies,with the mean classifi-cation accuracy of subjects up to nearly 65%. Furthermore,the recognition accuracies adopting characteristics of pre-paratory stage are superior to that of motorial stage,indicating that EEG evoked by movement preparation has abun-dant movement information and can be used for extracting control commands of active rehabilitation of brain-computer interface(BCI)system.