Increased Theta Oscillations During Motor Imagery in a Subject with Late-stage ALS.

Non-invasive brain computer interface (BCI) has been successfully used to control cursors, helicopters and robotic arms. However, this technology is not widely adopted by people with late-stage amyotrophic lateral sclerosis (ALS) due to poor effectiveness. In this study, we attempt to assess the cognitive state of a completely locked-in ALS subject, and her ability to use motor imagery-based BCI for control. The subject achieves above chance level accuracies for both open loop (62.2%) and closed-loop (68.7%) 2-class movement vs. idle decoding. We also observe a prominent theta oscillation with peak frequency at 4.5 Hz during the experiments. Quantification shows that the theta oscillatory power increases during motor imagery tasks compared to idle tasks for both open-loop as well as closed-loop BCI tasks. Furthermore, for closed-loop sessions, theta oscillation power correlates positively with feedback accuracy during movement tasks, and negatively with feedback accuracy during idle tasks. Our study demonstrates the feasibility of motor imagery-based BCI for late-stage ALS subjects, and highlights the importance of feedback during BCI implementation.