Gesture recognition is the key to human-computer interaction. In order to better realize the fusion of EEG and EMG signals and accurately recognize human motion, this paper established a research system of real-time gesture detection and recognition based on Grael EEG amplifier. The surface electromyography (sEMG) signals of eight different gestures from five channels were collected by Grael EEG amplifier and Curry8 system, and the collected sEMG signals were preprocessed by filtering and denoising, sliding window segmentation and feature extraction. Finally, several commonly used classifiers and convolutional neural network (CNN) are used to classify and recognize sEMG signals of different gestures in real time. The results show that the recognition accuracy of CNN is the highest, reaching 92.98%. After 30 times of real-time recognition and detection for each gesture, the results show that the recognition delay is about 1~1.5 s, and the accuracy of real-time recognition can be up to 90%. This system provides a feasible method to study the fusion of EEG and EMG signals in the future, and shows great potential and application space in human-computer interaction.