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

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