EEG Motor-Imagery BCI Steers Wheelchair Prototype, With SVM Decoding at 90.7%
A research team built an EEG motor-imagery brain-computer interface and connected its decoder to a physical differential-drive wheelchair prototype, offering people with severe motor impairments a non-invasive means of control, though the uncertainty of EEG decoding has long hindered such systems from driving physical actuators. Using Filter Bank Common Spatial Pattern features on a public dataset, the team compared support vector machine (SVM), k-nearest neighbors and linear discriminant analysis classifiers; SVM reached 90.7% mean accuracy, and 85.9% when replayed on an independent dataset through a hardware-in-the-loop setup. Confidence-based command validation raised accuracy among accepted commands to 97.6%, at the cost of accepting only 60.7% of them. Layered safeguards (confidence gating, self-terminating steering, transition braking, communication timeout supervision and a hardware emergency cutoff) limit the consequences of decoding errors.