Subject-specific feature extraction approach for a three-class motor imagery-based brain-computer interface enabling navigation in a virtual environment: open-access framework

• Published in: Biomedical physics & engineering express Vol. 11; no. 5; pp. 55001 - 55013
• Main Authors: Afdideh, Fardin, Shamsollahi, Mohammad Bagher
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 30.09.2025
• Subjects: Adult; Brain - physiology; brain-computer interface (BCI); Brain-Computer Interfaces; electroencephalogram (EEG); Electroencephalography - methods; Female; Humans; Imagination - physiology; Male; motor imagery (MI); Movement; subject-specific feature extraction; User-Computer Interface; Virtual Reality; virtual reality (VR); Young Adult; subject-specific feature extraction; virtual reality (VR); motor imagery (MI); brain-computer interface (BCI); electroencephalogram (EEG)
• ISSN: 2057-1976; 2057-1976
• DOI: 10.1088/2057-1976/aded19

Brain-Computer Interface (BCI) is a system that aids individuals with disabilities to establish a novel communication channel between the brain and computer. Among various electrophysiological sources that can drive a BCI system, Motor Imagery (MI) facilitates more natural communication for users with motor disabilities, whereas electroencephalogram (EEG) is considered the most practical brain imaging modality. However, subject training is a critical aspect of such a type of BCI. One possible solution to address this challenge is to leverage the Virtual Reality (VR) technology. This study proposes a VR in MI- and EEG-based BCI (MI-EEG-BCI-VR) framework wherein users navigate a Virtual Environment (VE) following cue-based training, and employing a subject-specific feature extraction approach. The assigned task involves performing the left hand, right hand, and feet movement imagination to navigate from the start station to the end station as quickly as possible. The generated brain signals are collected using three bipolar EEG channels only. The proposed open-access MATLAB-based MI-EEG-BCI-VR framework was validated with eight healthy participants. One participant demonstrated satisfactory performance in navigating the VE. Notably, it achieved the highest performance of 82.28 ± 5.11% for MI and 97.72 ± 4.55% for Motor Execution (ME) after just a single training session.