High Classification Accuracy of a Motor Imagery Based Brain-Computer Interface for Stroke Rehabilitation Training.

Motor imagery (MI) based brain-computer interfaces (BCI) extract commands in real-time and can be used to control a cursor, a robot or functional electrical stimulation (FES) devices. The control of FES devices is especially interesting for stroke rehabilitation, when a patient can use motor imagery to stimulate specific muscles in real-time. However, damage to motor areas resulting from stroke or other causes might impair control of a motor imagery BCI for rehabilitation. The current work presents a comparative evaluation of the MI-based BCI control accuracy between stroke patients and healthy subjects. Five patients who had a stroke that affected the motor system participated in the current study, and were trained across 10-24 sessions lasting about 1 h each with the recoveriX system. The participants' EEG data were classified while they imagined left or right hand movements, and real-time feedback was provided on a monitor. If the correct imagination was detected, the FES was also activated to move the left or right hand. The grand average mean accuracy was 87.4% for all patients and sessions. All patients were able to achieve at least one session with a maximum accuracy above 96%. Both the mean accuracy and the maximum accuracy were surprisingly high and above results seen with healthy controls in prior studies. Importantly, the study showed that stroke patients can control a MI BCI system with high accuracy relative to healthy persons. This may occur because these patients are highly motivated to participate in a study to improve their motor functions. Participants often reported early in the training of motor improvements and this caused additional motivation. However, it also reflects the efficacy of combining motor imagination, seeing continuous bar feedback, and real hand movement that also activates the tactile and proprioceptive systems. Results also suggested that motor function could improve even if classification accuracy did not, and suggest other new questions to explore in future work. Future studies will also be done with a first-person view 3D avatar to provide improved feedback and thereby increase each patients' sense of engagement.

Brain-Computer Interfaces With Multi-Sensory Feedback for Stroke Rehabilitation: A Case Study.

Conventional therapies do not provide paralyzed patients with closed-loop sensorimotor integration for motor rehabilitation. This work presents the recoveriX system, a hardware and software platform that combines a motor imagery (MI)-based brain-computer interface (BCI), functional electrical stimulation (FES), and visual feedback technologies for a complete sensorimotor closed-loop therapy system for poststroke rehabilitation. The proposed system was tested on two chronic stroke patients in a clinical environment. The patients were instructed to imagine the movement of either the left or right hand in random order. During these two MI tasks, two types of feedback were provided: a bar extending to the left or right side of a monitor as visual feedback and passive hand opening stimulated from FES as proprioceptive feedback. Both types of feedback relied on the BCI classification result achieved using common spatial patterns and a linear discriminant analysis classifier. After 10 sessions of recoveriX training, one patient partially regained control of wrist extension in her paretic wrist and the other patient increased the range of middle finger movement by 1 cm. A controlled group study is planned with a new version of the recoveriX system, which will have several improvements.

Paired Associative Stimulation Using Brain-Computer Interfaces for Stroke Rehabilitation: A Pilot Study.

Conventional therapies do not provide paralyzed patients with closed-loop sensorimotor integration for motor rehabilitation. Paired associative stimulation (PAS) uses brain-computer interface (BCI) technology to monitor patients' movement imagery in real-time, and utilizes the information to control functional electrical stimulation (FES) and bar feedback for complete sensorimotor closed loop. To realize this approach, we introduce the recoveriX system, a hardware and software platform for PAS. After 10 sessions of recoveriX training, one stroke patient partially regained control of dorsiflexion in her paretic wrist. A controlled group study is planned with a new version of the recoveriX system, which will use a new FES system and an avatar instead of bar feedback.