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

Brain computer interfaces (BCI) require realtime detection of conscious EEG changes so that a user can for example control a video game. However scalp recordings are contaminated with noise, in particular muscle activity and eye movement artefacts. This noise is non-stationary because subjects will voluntarily or involuntarily use their facial muscles or move their eyes. If such non-stationary noise is powerful enough a detector will no longer be able to distinguish between signal and noise which is called SNR-wall. As a relevant and instructional example for BCI we have recorded scalp signals from the central electrode Cz during 8 different activities ranging from relaxed, over playing a video game to reading out loud. We then filtered the raw signals using four different postprocessing scenarios which are popular in the BCI-literature. The results show that filtering with a 1st order highpass makes it impossible to detect conscious EEG changes during any of the physical activities. A wideband bandpass filter between 8-18 Hz allows the detection of conscious EEG changes while playing a phone app, Sudoku, word search and colouring. Reducing the bandwidth during postprocessing to 8-12 Hz allows additionally conscious detection of EEG during reading out loud. The SNR-wall applied to BCI gives now a hard and measurable criterion to determine if a BCI experiment can detect conscious EEG changes or not. It enables one to flag up experimental setups and postprocessing scenarios where misinterpreting of scalp recordings is highly likely, in particular misinterpreting non-stationary muscle activity as conscious EEG changes.