融合频带能量特征和双向门控循环单元的运动想象意图识别

针对脑电信号分类正确率低的问题,结合频带能量、小波包变换和双向门控循环网络,提出了一种基于频带能量特征序列和深度学习算法的运动想象意图识别方法。首先,利用小波包变换对脑电信号进行分解、重构,获得运动想象相关频带信号;其次,对所得频带信号进行加窗,并滑动截取,通过计算所截每段信号能量,实现能量特征的时序化分解;最后利用双向门控循环网络对脑电信号进行识别并输出分类结果。实验结果表明:所提算法取得了92.1%的分类正确率,表明所提方法是切实可行的,能够有效改善分类识别率。

Intention Recognition of Motor Imagery Combined Multiband Power FeaturesSand BiGRU

Due to the low classification accuracy of EEG signal, a novel method based on band power time-series and deep learning algorithm was proposed, which fused wavelet packet transform, band power features and bidirectional gated recurrent unit( BiGRU). Firstly, the band signals, which were related to motor imagery, were obtained by using wavelet packet transformation to decomposition and reconstruction the raw EEG data. Secondly, Calculating the power of each EEG epoch cropped by a sliding time-window, the sequenced feature vectors were achieved. Finally, a BiGRU model was used to classify the EEG signals and output the classification results. Experimental results show that 92.1% accuracy rate is achieved by using the proposed algorithm, which indicates the developed novel method is feasible and can improve the recognition rate effectively for BCI system.