Combining ERD and ERS features to create a system-paced BCI

• Brief motor-imagery (MI) and post-MI features are exploited to design a brain computer interface (BCI).
• Data from 3 electrodes are used to discriminate between 2 intentional control tasks and a no-control state.
• The patterns used by BCI are demonstrated to be robust over time and are compatible with discrete feedback.
• The results are validated by an online study performed in our laboratory with 6 subjects.

An important factor in the usability of a brain–computer interface (BCI) is the setup and calibration time required for the interface to perform accurately. Recently, brain-switches based on the beta rebound following motor imagery of a single limb effector have been investigated as basic BCIs due to their good performance with limited electrodes, and brief training session requirements. Here, a BCI is proposed which expands the methodology of brain-switches to design an interface composed of multiple brain-controlled buttons. The algorithm is designed as a system paced interface which can recognise 2 intentional-control tasks and a no-control state based on the activity during and following motor imagery in only 3 electroencephalogram channels. An online experiment was performed over 6 subjects to validate the algorithm, and the results show that a working BCI can be trained from a single calibration session and that the post motor imagery features are both informative and robust over multiple sessions.