CAJ | 학술논문

针对运动想象脑电信号在经验模态分解（EMD）后人为选取固有模态函数（IMF）导致重构信号混入噪声且丢失有用信息的问题，提出一种改进希尔伯特‐黄变换（HHT）和样本熵结合的特征提取方法。在原始脑电信号经过EMD后，计算各个IM F与原始信号的相关系数以及IM F中瞬时频率在μ／β节律频带内的个数，提取有效IM F的能量均值，联合计算脑电信号的样本熵构成特征向量，采用支持向量机（SVM ）分类器对提取的特征进行分类，在智能轮椅平台上对算法进行验证。验证结果表明，采用改进 HHT结合样本熵的智能轮椅系统有较高正确识别率，稳定性更好。
침대운동상상뇌전신호재경험모태분해（EMD）후인위선취고유모태함수（IMF）도치중구신호혼입조성차주실유용신식적문제，제출일충개진희이백특‐황변환（HHT）화양본적결합적특정제취방법。재원시뇌전신호경과EMD후，계산각개IM F여원시신호적상관계수이급IM F중순시빈솔재μ／β절률빈대내적개수，제취유효IM F적능량균치，연합계산뇌전신호적양본적구성특정향량，채용지지향량궤（SVM ）분류기대제취적특정진행분류，재지능륜의평태상대산법진행험증。험증결과표명，채용개진 HHT결합양본적적지능륜의계통유교고정학식별솔，은정성경호。
After processing the motor imagery electroencephalogram (EEG) signal by empirical mode decomposition (EMD) ,in‐trinsic mode functions (IMF) are artificially selected ,which results in mixing the reconstructed signal with noise and lossing use‐ful information .Therefore the feature extraction method of combining improved Hilbert‐Huang transform (HHT) and sample entropy was proposed .After the original EEG signal was processed using EMD ,each IMF and the original signal were used to calculate the correlation coefficient and the number of IMF’s instantaneous frequency belonging to the μ/βrhythm band .The average energy of the effective IMF was extracted and the sample entropy of EEG signal was calculated to constitute the feature vectors .The extracted features were classified by using support vector machine (SVM ) classifier .And the algorithm was veri‐fied on the smart wheelchair platform .The results show that the wheelchair system based on improved HHT and sample entropy has higher correct recognition rate and better stability .