  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.
