Rehabilitation Treatment of Motor Dysfunction Patients Based on Deep Learning Brain–Computer Interface Technology - Wang, Huihai; Su, Qinglun; Yan, Zhenzhuang; Lu, Fei; Zhao, Qin; Liu, Zhen; Zhou, Fang (Frontiers in Neuroscience, 2020)
In the decades since the birth of brain-computer interface technology, the research on the classification of EEG has been the driving force to promote the continuous development of brain-computer interface technology. EEG has the characteristics of low signal-to-noise ratio, vulnerable to interference, and obvious differences between different individuals, which makes it difficult for traditional classification methods to find good differentiation and representative characteristics to design a classification model with excellent performance. With the above characteristics, brain-computer interface technology is expected to solve the physiological and psychological needs of patients with motor dysfunction with great individual differences. However, the classification method based on feature extraction requires a lot of prior knowledge when extracting data features and lacks a good measurement standard, which makes the development of brain-computer interface technology. In particular, the development of a multi-classification brain-computer interface is facing a bottleneck. However, in recent years, with the characteristics of layer-by-layer automatic learning data features, step-by-step abstraction, and good generalization ability, deep learning has achieved great success in the field of image and speech. To avoid the blindness and complexity of EEG feature extraction, the deep learning method is applied to the automatic feature extraction of EEG signals. It is necessary to design a classification model with strong robustness and high accuracy for EEG signals. Based on the research and implementation of a brain-computer interface system based on a convolutional neural network, this paper aims to design a brain-computer interface system that can automatically extract features of EEG signals and classify EEG signals accurately. It can avoid the blindness and time-consuming problems caused by the machine learning method based on feature extraction in feature extraction of EEG data due to the lack of a large amount of prior knowledge. The purpose of this paper is to explore the extension of deep learning methods in the field of brain-computer interface.