Self-paced brain-computer interface control of ambulation in a virtual reality environment

TL;DR

This study demonstrates a novel EEG-based brain-computer interface that enables intuitive, self-paced control of virtual ambulation with minimal training, showing promise for future prosthetic applications for individuals with spinal cord injury.

Contribution

Developed and tested a data-driven, EEG-based BCI system for self-paced virtual ambulation control, demonstrating high accuracy and minimal training requirements.

Findings

Average offline decoding accuracy was 77.2%.

Subjects successfully controlled avatar ambulation with significant performance.

System achieved high success in goal-oriented tasks across sessions.

Abstract

Objective: Spinal cord injury (SCI) often leaves affected individuals unable to ambulate. Electroencephalogramme (EEG) based brain-computer interface (BCI) controlled lower extremity prostheses may restore intuitive and able-body-like ambulation after SCI. To test its feasibility, the authors developed and tested a novel EEG-based, data-driven BCI system for intuitive and self-paced control of the ambulation of an avatar within a virtual reality environment (VRE). Approach: Eight able-bodied subjects and one with SCI underwent the following 10-min training session: subjects alternated between idling and walking kinaesthetic motor imageries (KMI) while their EEG were recorded and analysed to generate subject-specific decoding models. Subjects then performed a goal-oriented online task, repeated over 5 sessions, in which they utilised the KMI to control the linear ambulation of an avatar and make 10 sequential stops at designated points within the VRE. Main results: The average offline training performance across subjects was 77.2 +/- 9.5%, ranging from 64.3% (p = 0.00176) to 94.5% (p = 6.26*10^-23), with chance performance being 50%. The average online performance was 8.4 +/- 1.0 (out of 10) successful stops and 303 +/- 53 sec completion time (perfect = 211 sec). All subjects achieved performances significantly different than those of random walk (p < 0.05) in 44 of the 45 online sessions. Significance: By using a data-driven machine learning approach to decode users' KMI, this BCIVRE system enabled intuitive and purposeful self-paced control of ambulation after only a 10-minute training. The ability to achieve such BCI control with minimal training indicates that the implementation of future BCI-lower extremity prosthesis systems may be feasible.