Wavelet Transform Time-Frequency Image and Convolutional Network-Based Motor Imagery EEG Classification

Feature extraction and classification play an important role in brain–computer interface (BCI) systems. In traditional approaches, methods in pattern recognition field are adopted to solve these problems. Nowadays, the deep learning theory has developed so fast that researchers have employed it in many areas like computer vision and speech recognition, which have achieved remarkable results. However, few people introduce the deep learning method into the study of biomedical signals, especially EEG signals. In this paper, a wavelet transform-based input, which combines the time-frequency images of C3, Cz, and C4 channels, is proposed to extract the feature of motor imagery EEG signal. Then, a 2-Layer convolutional neural network is built as the classifier and convolutional kernels of different sizes are validated. The performance obtained by the proposed approach is evaluated by accuracy and Kappa value. The accuracy on dataset III from BCI competition II reaches 90%, and the best Kappa value on dataset 2a from competition IV is greater than many of other methods. In addition, the proposed method utilizes a resized small input, which reduces calculation complexity, so the training period is relatively faster. The results show that the method using convolutional neural network can be comparable or better than the other state-of-the-art approaches, and the performance will be improved when there is sufficient data.