A brain-computer interface offers a mean for communication and control that is alternative to normal brain pathways. Among the several paradigms under study, motor imagery-based BCIs (MI-BCI) rely on spontaneous sensorimotor rhythms and do not need for external stimuli in eliciting the brain activity to be measured. In this context, neurofeedback is essential in helping the user to better focus the mental task, and hence guarantee proper functionality of the system. Many BCI technologies rely on vision for their functionality. The need to investigate further feedback paradigms arises in aiming to create a more immersive experience, which would lead to a stronger engagement of the user. In particular, haptic feedback is of great interest because it is claimed that it could naturally close the sensorimotor loop.
Therefore, in this project the brain activity will be measured by means of electroencephalography (EEG), with few dry electrodes placed around the sensorimotor area and neurofeedback will be provided in real-time in accordance with the measured biosignals. Attention will be given to the algorithm for online classification of EEG signals, in which the information about sensorimotor rhythms is encoded. The proposed technology could find interesting applications in rehabilitation, gaming, and even in robotics.