A multi-modal brain–computer interface based on threshold discrimination and its application in wheelchair control

• Published in: Cognitive neurodynamics Vol. 16; no. 5; pp. 1123 - 1133
• Main Authors: Dong, Enzeng, Zhang, Haoran, Zhu, Lin, Du, Shengzhi, Tong, Jigang
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.10.2022; Springer Nature B.V
• Subjects: Artificial Intelligence; Biochemistry; Biomedical and Life Sciences; Biomedicine; Brain; Classification; Cognitive Psychology; Communication channels; Computer applications; Computer Science; Correlation analysis; Electrodes; Electroencephalography; Human-computer interface; Implants; Mental task performance; Movement; Neurosciences; Obstacle avoidance; Research Article; Signal processing; Task complexity; Wheelchairs; Steady-state visual evoked potential (SSVEP); Threshold discrimination; Multi-modal EEG signals; Threshold strategy; Motor imagination (MI); BCI controlled wheelchair; Brain–computer interface (BCI)
• ISSN: 1871-4080; 1871-4099
• DOI: 10.1007/s11571-021-09779-7

In this study, we propose a novel multi-modal brain–computer interface (BCI) system based on the threshold discrimination, which is proposed for the first time to distinguish between SSVEP and MI potentials. The system combines these two heterogeneous signals to increase the number of control commands and improve the performance of asynchronous control of external devices. In this research, an electric wheelchair is controlled as an example. The user can continuously control the wheelchair to turn left/right through motion imagination (MI) by imagining left/right-hand movement and generate another 6 commands for the wheelchair control by focusing on the SSVEP stimulation panel. Ten subjects participated in a MI training session and eight of them completed a mobile obstacle-avoidance experiment in a complex environment requesting high control accuracy for successful manipulation. Comparing with the single-modal BCI-controlled wheelchair system, the results demonstrate that the proposed multi-modal method is effective by providing more satisfactory control accuracy, and show the potential of BCI-controlled systems to be applied in complex daily tasks.