A portable steady-state visual evoked potential brain-computer interface system for smart healthcare.
10.7507/1001-5515.202412051
- Author:
Yisen ZHU
1
;
Zhouyu JI
1
;
Shuran LI
2
;
Haicheng WANG
1
;
Yunfa FU
3
;
Hongtao WANG
1
Author Information
1. College of Electronics and Information Engineering, Wuyi University, Jiangmen, Guangdong 529020, P. R. China.
2. School of Electronic & Information Engineering and Communication Engineering, Guangzhou City University of Technology, Guangdong 510800, P. R. China.
3. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.
- Publication Type:Journal Article
- Keywords:
Brain-computer interface;
Filter bank canonical correlation analysis;
Medical rehabilitation assistance;
Steady-state visual evoked potential
- MeSH:
Brain-Computer Interfaces;
Humans;
Evoked Potentials, Visual/physiology*;
Electroencephalography;
Signal Processing, Computer-Assisted;
Software;
Adult;
Male
- From:
Journal of Biomedical Engineering
2025;42(3):455-463
- CountryChina
- Language:Chinese
-
Abstract:
This paper realized a portable brain-computer interface (BCI) system tailored for smart healthcare. Through the decoding of steady-state visual evoked potential (SSVEP), this system can rapidly and accurately identify the intentions of subjects, thereby meeting the practical demands of daily medical scenarios. Firstly, an SSVEP stimulation interface and an electroencephalogram (EEG) signal acquisition software were designed, which enable the system to execute multi-target and multi-task operations while also incorporating data visualization functionality. Secondly, the EEG signals recorded from the occipital region were decomposed into eight sub-frequency bands using filter bank canonical correlation analysis (FBCCA). Subsequently, the similarity between each sub-band signal and the reference signals was computed to achieve efficient SSVEP decoding. Finally, 15 subjects were recruited to participate in the online evaluation of the system. The experimental results indicated that in real-world scenarios, the system achieved an average accuracy of 85.19% in identifying the intentions of the subjects, and an information transfer rate (ITR) of 37.52 bit/min. This system was awarded third prize in the Visual BCI Innovation Application Development competition at the 2024 World Robot Contest, validating its effectiveness. In conclusion, this study has developed a portable, multifunctional SSVEP online decoding system, providing an effective approach for human-computer interaction in smart healthcare.
