As more and more people are left disabled by stroke each year, it is of vital importance to progress in the research of new ways to improve their condition and to ensure that they maintain their independence as much as possible in everyday life. A step in this direction of research was taken with TRAVEE, a system dedicated to neuromotor rehabilitation after stroke. To reach this goal, the TRAVEE has benefited from several innovative ideas and technologies-virtual reality, brain-computer interfaces, functional electrical stimulation, robotics, haptics, multimodal feedback, and a novel idea in information and communications technology systems for rehabilitation-visual augmentation as a form of feedback to the patient. Through visual augmentation, the TRAVEE immerses the patient in a virtual environment where his movements are rendered as being better than in the real world, and in this way diminishing his disability. We believe that this process-that is pending for patent-will greatly impact the recovery process after stroke, by providing more motivating sessions, while supporting the cortical reorganization process. This paper presents an overview of the TRAVEE system, the perspectives that supported it, details regarding its development, as well as the results of the clinical tests that were performed with the system.

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 interface (BCI) systems have been used primarily to provide communication for persons with severe movement disabilities. This paper presents a new system that extends BCI technology to a new patient group: persons diagnosed with stroke. This system, called recoveriX, is designed to detect changes in motor imagery in real-time to help monitor compliance and provide closed-loop feedback during therapy. We describe recoveriX and present initial results from one patient.

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