Design of Brain Computer Interface Control System Based on Neural Network

Abstract

As an interdisciplinary technology, Brain Computer Interface(BCI) combines the knowledge of neuroscience, biomedical engineering, machine learning and other fields, and is gradually changing people's life and work style. The purpose of this article is to study the brain wave signal identification method based on convolutional neural network (CNN). Firstly, advanced data processing methods are used to preprocess electroencephalogram (EEG) data, including noise removal and baseline drift correction, so as to improve the signal-to-noise ratio and characteristics of the data. Then, the CNN model is constructed, and by optimizing the model structure and parameters, it can better adapt to the processing of EEG data. Finally, compared with other neural network algorithms, the results show that the accuracy of CNN algorithm adopted in this article is higher than other algorithms in training set and test set, and the performance is the best. This algorithm can improve the recognition accuracy of EEG signals, thus achieving a more efficient and accurate BCI control system. The research provides new ideas and methods for EEG signal processing based on neural network, and can provide technical support for the design and implementation of BCI control system.