Abstract

In recent years, brain–computer interface (BCI) 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 BCI. In particular, the development of a multi-classification brain–computer interface is facing a bottleneck. To avoid the blindness and complexity of electroencephalogram (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 BCI system based on a convolutional neural network, this article 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 of EEG data due to the lack of a large amount of prior knowledge.

Highlights

  • Brain–computer interface (BCI) is a communication control system established between the brain and external devices through signals generated by brain activity

  • Based on the research and implementation of a brain–computer interface system based on a convolutional neural network, this article aims to design a brain–computer interface system that can automatically extract features of EEG signals and classify EEG signals accurately

  • The EEG signals in the previous section are classified according to different motor imagination tasks, in which there are 5140,700 samples of imagining left finger movement and imagining right finger movement, and each sample is a matrix with a dimension

Read more

Summary

Introduction

Brain–computer interface (BCI) is a communication control system established between the brain and external devices (computers or other electronic devices) through signals generated by brain activity. The system does not depend on muscles and nerves other than the brain and establishes direct communication between the brain and the machine (Wang et al, 2020). It is a new, highend way of human–computer interaction. The BCI system can monitor the signals generated by neural activities through a variety of sensors and other signal acquisition equipment.

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call