Performance evaluation of a wearable steady-state visual evoked potential based brain-computer interface in real-life scenario.
10.7507/1001-5515.202310069
- Author:
Xiaodong LI
1
;
Xiang CAO
2
;
Junlin WANG
3
;
Weijie ZHU
4
;
Yong HUANG
5
;
Feng WAN
6
;
Yong HU
3
Author Information
1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, P. R. China.
2. Department of Orthopaedics and Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong S. A. R. 999077, P. R. China.
3. Orthopedic Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong 518053, P. R. China.
4. Joint Lab of Brain-Verse Digital Convergence, Guangdong Institute of Intelligence Science and Technology, Zhuhai, Guangdong 519060, P. R. China.
5. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, P. R. China.
6. Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau S. A. R. 999078, P. R. China.
- Publication Type:Journal Article
- Keywords:
Brain-computer interface;
Simplified experimental preparation;
Steady-state visual evoked potential;
Wearable system
- MeSH:
Brain-Computer Interfaces;
Humans;
Evoked Potentials, Visual/physiology*;
Electroencephalography;
Wearable Electronic Devices;
Algorithms;
Signal Processing, Computer-Assisted;
Adult;
Male
- From:
Journal of Biomedical Engineering
2025;42(3):464-472
- CountryChina
- Language:Chinese
-
Abstract:
Brain-computer interface (BCI) has high application value in the field of healthcare. However, in practical clinical applications, convenience and system performance should be considered in the use of BCI. Wearable BCIs are generally with high convenience, but their performance in real-life scenario needs to be evaluated. This study proposed a wearable steady-state visual evoked potential (SSVEP)-based BCI system equipped with a small-sized electroencephalogram (EEG) collector and a high-performance training-free decoding algorithm. Ten healthy subjects participated in the test of BCI system under simplified experimental preparation. The results showed that the average classification accuracy of this BCI was 94.10% for 40 targets, and there was no significant difference compared to the dataset collected under the laboratory condition. The system achieved a maximum information transfer rate (ITR) of 115.25 bit/min with 8-channel signal and 98.49 bit/min with 4-channel signal, indicating that the 4-channel solution can be used as an option for the few-channel BCI. Overall, this wearable SSVEP-BCI can achieve good performance in real-life scenario, which helps to promote BCI technology in clinical practice.
