机器人学研究室知识库

基于运动想象脑电（EEG）的脑-机接口系统能够为用户提供更为自然、灵活的控制方式，已广泛应用到人机交互领域。然而，由于目前运动想象脑电的信噪比及空间分辨率较低，导致信号解码正确率较低。针对这一问题，本文提出一种基于时空特征学习卷积神经网络（TSCNN）的运动想象脑电解码方法。首先，针对经过带通滤波预处理的脑电信号，依次设计时间和空间维度上的卷积层，构造出运动想象脑电的时空特征；然后，利用2层二维卷积结构对脑电的时空特征进行抽象学习；最后，通过全连接层和Softmax层对TSCNN学习的抽象特征进行解码。利用公开数据集对该方法进行实验测试，结果表明，所提方法的平均解码精度达到80.09%，分别比经典的解码方法共空间模式（CSP）+支持向量机（SVM）和滤波器组CSP(FBCSP)+SVM提高了13.75%和10.99%，显著提升了运动想象脑电解码的可靠性。

With the advantage of providing more natural and flexible control manner, brain-computer interface systems based on motor imagery electroencephalogram (EEG) have been widely used in the field of human-machine interaction. However, due to the lower signal-noise ratio and poor spatial resolution of EEG signals, the decoding accuracy is relative low. To solve this problem, a novel convolutional neural network based on temporal-spatial feature learning (TSCNN) was proposed for motor imagery EEG decoding. Firstly, for the EEG signals preprocessed by band-pass filtering, a temporal-wise convolution layer and a spatial-wise convolution layer were respectively designed, and temporal-spatial features of motor imagery EEG were constructed. Then, 2-layer two-dimensional convolutional structures were adopted to learn abstract features from the raw temporal-spatial features. Finally, the softmax layer combined with the fully connected layer were used to perform decoding task from the extracted abstract features. The experimental results of the proposed method on the open dataset showed that the average decoding accuracy was 80.09%, which is approximately 13.75% and 10.99% higher than that of the state-of-the-art common spatial pattern (CSP) + support vector machine (SVM) and filter bank CSP (FBCSP) + SVM recognition methods, respectively. This demonstrates that the proposed method can significantly improve the reliability of motor imagery EEG decoding.