Short-Latency Brain-Computer Interface Using Movement-Related Cortical Potentials

In recent years, brain-computer interface (BCI) has been developing as a communication and rehabilitation tool for patients with neurological diseases, such as stroke and spinal cord injury. This system decodes the user’s intention from the brain signal, e.g. electroencephalography (EEG), and translates it into device commands. For either rehabilitation or communication system based on BCI, the latency of decoding algorithms is the fundamental factor which directly determines the system’s performance. In this thesis, movement-related cortical potentials (MRCP) were investigated as the signal modality for real-time detection of motor intention from EEG. Aiming at a short-latency brain switch based on MRCP, a manifold leaning based method was developed, yielding a true positive rate > 80 % and a latency < 300 ms, which significantly outperformed previous methods. Based on this brain switch, two closed-loop BCI systems were implemented for rehabilitation and communication purpose, respectively. In the rehabilitation system, the brain switch was used to trigger a motorized ankle-foot orthosis, mimicking real movement of dorsiflexion, for natural afferent feed-