Stability of a chronic implanted brain-computer interface in late-stage amyotrophic lateral sclerosis. Issue 10 (October 2019) - Pels, Elmar G.M.; Aarnoutse, Erik J.; Leinders, Sacha; Freudenburg, Zac V.; Branco, Mariana P.; van der Vijgh, Benny H.; Snijders, Tom J.; Denison, Timothy; Vansteensel, Mariska J.; Ramsey, Nick F.
Highlights: Stability of recorded neural signals is crucial for the clinical viability of implantable brain-computer interfaces (BCIs). A fully implantable BCI offers a long-term robust and durable control signal in a person with amyotrophic lateral sclerosis. Increasing home use of the electrocorticography (ECoG)-BCI demonstrates user adoption of the BCI for communication. Abstract: Objective: We investigated the long-term functional stability and home use of a fully implanted electrocorticography (ECoG)-based brain-computer interface (BCI) for communication by an individual with late-stage Amyotrophic Lateral Sclerosis (ALS). Methods: Data recorded from the cortical surface of the motor and prefrontal cortex with an implanted brain-computer interface device was evaluated for 36 months after implantation of the system in an individual with late-stage ALS. In addition, electrode impedance and BCI control accuracy were assessed. Key measures included frequency of use of the system for communication, user and system performance, and electrical signal characteristics. Results: User performance was high consistently over the three years. Power in the high frequency band, used for the control signal, declined slowly in the motor cortex, but control over the signal remained unaffected by time. Impedance increased until month 5, and then remained constant. Frequency of home use increased steadily, indicating adoption of the system by the user. Conclusions: The implanted brain-computer interface proves to be robust in an individual with late-stage ALS, given stable performance and control signal for over 36 months. Significance: These findings are relevant for the future of implantable brain-computer interfaces along with other brain-sensing technologies, such as responsive neurostimulation.