BCI-Walls: A robust methodology to predict success or failure in brain computer interfaces

TL;DR

This paper introduces a new methodology for predicting the success of brain-computer interfaces by assessing the signal-to-noise ratio in EEG recordings amidst non-stationary noise, ensuring reliable detection of conscious brain activity.

Contribution

The paper presents a novel criterion based on the SNR-wall to determine when EEG signals are reliably detectable despite noise, improving BCI robustness.

Findings

Facial muscle activity and eye movements significantly affect EEG detectability.

Minimizing eye-movement and muscle noise is crucial for reliable EEG-based BCI detection.

The methodology provides a measurable criterion for EEG detectability in noisy conditions.

Abstract

Brain computer interfaces (BCI) depend on reliable realtime detection of conscious EEG changes for example to control a video game. However, scalp recordings are contaminated with non-stationary noise, such as facial muscle activity and eye movements. This interferes with the detection process making it potentially unreliable or even impossible. We have developed a new methodology which provides a hard and measurable criterion if conscious EEG changes can be detected in the presence of non-stationary noise by requiring the signal-to-noise ratio of a scalp recording to be greater than the SNR-wall which in turn is based on the highest and lowest noise variances of the recording. As an instructional example, we have recorded signals from the central electrode Cz during eight different activities causing non-stationary noise such as playing a video game or reading out loud. The results show that facial muscle activity and eye-movements have a strong impact on the detectability of EEG and that minimising both eye-movement artefacts and muscle noise is essential to be able to detect conscious EEG changes.