PREdicting Performance of sensorimotor brain-computer interface control

Diseases such as amyotrophic lateral sclerosis may cause severe paralysis and leave people effectively locked in their body: completely unable to move or speak, yet cognitively unaffected (locked-in syndrome). Brain-computer interfaces (BCIs) are being developed to restore communication abilities that were lost due to such severe paralysis. However, BCIs do not work equally efficient in every patient. For eventual wide clinical application of BCIs based on implanted brain-surface electrodes, it will be important to predict whether or not a certain individual has a high chance of achieving accurate control over the BCI before deciding to perform surgery to implant the BCI. This needs to be accomplished using non-invasive means. Measurements from the scalp could provide a rapid and easy approach to determine the quality of the neural signal changes prior to BCI implantation.The primary goal of this study is to assess whether or not measurements from the scalp can be used to predict control performance of (to-be) implanted BCIs. Specifically, we will determine if scalp recordings can be used to detect anomalies in the neural activity of individuals with severe motor-impairment.

The hypothesis is that measurements from the scalp can be used to predict control performance of (to-be) implanted BCIs.

A single EEG measurement session, typically at the day of informed consent.