Key technologies for intelligent brain-computer interaction based on magnetoencephalography.
10.7507/1001-5515.202108069
- Author:
Haotian XU
1
;
Anmin GONG
2
;
Peng DING
1
;
Jiangong LUO
1
;
Chao CHEN
3
;
Yunfa FU
1
Author Information
1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.
2. School of Information Engineering, Chinese People's Armed Police Force Engineering University, Xi'an 710000, P. R. China.
3. Tianjin University of Technology School of Electrical and Electronic Engineering, Tianjin 300000, P. R. China.
- Publication Type:Journal Article
- Keywords:
Brain-computer interface;
Intelligent brain-computer interface;
Magnetoencephalography;
Magnetoencephalography feature extraction.;
Magnetoencephalography-brain-computer interface experimental paradigm design
- MeSH:
Brain/physiology*;
Brain-Computer Interfaces;
Electroencephalography;
Humans;
Imagery, Psychotherapy;
Magnetoencephalography;
Technology
- From:
Journal of Biomedical Engineering
2022;39(1):198-206
- CountryChina
- Language:Chinese
-
Abstract:
Brain-computer interaction (BCI) is a transformative human-computer interaction, which aims to bypass the peripheral nerve and muscle system and directly convert the perception, imagery or thinking activities of cranial nerves into actions for further improving the quality of human life. Magnetoencephalogram (MEG) measures the magnetic field generated by the electrical activity of neurons. It has the unique advantages of non-contact measurement, high temporal and spatial resolution, and convenient preparation. It is a new BCI driving signal. MEG-BCI research has important brain science significance and potential application value. So far, few documents have elaborated the key technical issues involved in MEG-BCI. Therefore, this paper focuses on the key technologies of MEG-BCI, and details the signal acquisition technology involved in the practical MEG-BCI system, the design of the MEG-BCI experimental paradigm, the MEG signal analysis and decoding key technology, MEG-BCI neurofeedback technology and its intelligent method. Finally, this paper also discusses the existing problems and future development trends of MEG-BCI. It is hoped that this paper will provide more useful ideas for MEG-BCI innovation research.
