A Robust Low-Cost EEG Motor Imagery-Based Brain-Computer Interface

TLDR

Motor‑imagery BCIs can give locked‑in patients independence, but existing high‑cost EEG systems are impractical for home use and prior low‑cost studies have shown only limited performance. The study aimed to evaluate OpenBCI in a natural setting and enhance its performance using neurofeedback, deep learning, and extended temporal windows. We trained a multi‑layer perceptron on μ‑rhythm EEG from healthy subjects performing relaxation and right‑handed motor imagery. Our approach outperformed prior OpenBCI motor‑imagery BCIs, extending the device’s capabilities.

Abstract

Motor imagery (MI) based Brain-Computer Interfaces (BCIs) are a viable option for giving locked-in syndrome patients independence and communicability. BCIs comprising expensive medical-grade EEG systems evaluated in carefully-controlled, artificial environments are impractical for take-home use. Previous studies evaluated low-cost systems; however, performance was suboptimal or inconclusive. Here we evaluated a low-cost EEG system, OpenBCI, in a natural environment and leveraged neurofeedback, deep learning, and wider temporal windows to improve performance. μ-rhythm data collected over the sensorimotor cortex from healthy participants performing relaxation and right-handed MI tasks were used to train a multi-layer perceptron binary classifier using deep learning. We showed that our method outperforms previous OpenBCI MI-based BCIs, thereby extending the BCI capabilities of this low-cost device.