A new visual feed-back modality for the reduction of artifacts in mu-rhythm based brain-computer interfaces

A common problem in EEG recording sessions is that results can be heavily contaminated by artifacts. One of the main reasons is that eyes movements generate a noise signal that superimpose to the data. In some BCI protocols the user has generally to control the movement of a cursor on a PC screen by self-regulating his/her mu-rhythm. In general this requires the user to move the eyes to follow the same cursor, thus intrinsically generating a huge amount of noise. To overcome this problem a new feedback modality has been developed, which is able to dramatically reduce the artifacts as it does not require subjects to move their eyes.