Research On Natural Human-Computer Interaction System Based On Brain-Computer Interface

With the proposal of The Chinese Thirteenth Five-Year Plan,brain science and brain-like research have become more and more important in human life.Brain-computer interface technology provides a more convenient channel for human beings to realize the exchange and control of EEG signals with the outside world.The technology of human-machine interaction is the development trend of future computing,it will gradually infiltrate into all walks of life.Based on the above analysis of the subject,a natural human-machine interaction system based on brain-computer interface is studied.This research content mainly includes the following three parts:1.The secondary development of Mind Wave equipment are used to collected the brain electrical signals generated by the body movements,and the collected EEG signals were filtered by FIR digital filter to remove artifacts and improve it's signal-to-noise ratio.2.The filtered EEG signals are extracted using wavelet energy entropy and AR model algorithm,and the extracted signals are identified and classified by the constraint bound learning machine and convolutional neural network,and the identified signals are analyzed.The analysis results show that the eigenvectors obtained by combining the wavelet energy entropy and the AR parametric model are classified by a constraint learning machine.The classification effect is relatively good and the classification accuracy rate reaches 87.5%.3.The human-computer interaction system is designed and verified.During the verification process,the controlled object is an existing drone,and the identified results are used to translate into specific control instructions to drive the flying state of the drone.The verification results show that the system has efficient interaction efficiency.This article mainly studied a variety of algorithms about EEG signal feature extraction and recognition,compares and analyzes the results of various algorithms,selects the optimal algorithm and applies it to human-computer interaction systems,and ultimately achieves the goal of improving human-computer interaction efficiency.