Using a Hybrid Brain Computer Interface and Virtual Reality System to Monitor and Promote Cortical Reorganization through Motor Activity and Motor Imagery Training

Abstract

Abstract-Stroke is one of the leading causes of adult disability with high economical and societal costs. In recent years, novel rehabilitation paradigms have been proposed to address the life-long plasticity of the brain to regain motor function. We propose a hybrid brain-computer interface (BCI)-virtual reality (VR) system that combines a personalized motor training in a VR environment, exploiting brain mechanisms for action execution and observation, and a neuro-feedback paradigm using mental imagery as a way to engage secondary or indirect pathways to access undamaged cortico-spinal tracts. Furthermore, we present the development and validation experiments of the proposed system. More specifically, EEG data on nine naïve healthy subjects show that a simultaneous motor activity and motor imagery paradigm is more effective at engaging cortical motor areas and related networks to a larger extent. Additionally, we propose a motor imagery driven BCI-VR version of our system that was evaluated with nine different healthy subjects. Data show that users are capable of controlling a virtual avatar in a motor imagery training task that dynamically adjusts its difficulty to the capabilities of the user. User self-report questionnaires indicate enjoyment and acceptance of the proposed system. Index Terms-Brain-computer interface (BCI), mirror neurons, motor imagery, neuro-feeback, personalized training, stroke, virtual reality.

Novel Virtual Reality Application in Field of Neurorehabilitation

“…For example, a subject could be trained to modulate, augment or lessen, a specific brain signal or rhythm through performing tasks in VR environments giving feedbacks according to the brain signal or rhythm. Using EEG based BCI paradigm for motor activity and motor imagery, it can provide a neurofeedback training and promote brain reorganization after stroke [32]. Other Researchers used a flickering action video to evoke steady state visual evoked potential (SSVEP).…”

Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose

“…Steady-state error (e ss ): The difference between a given set point and the time-response of the plant (12) B t = log 2 N + P log 2 P + (1 − P) log 2 1 − P N − 1 × 60 T configured in a closed loop feedback system after a large (theoretically infinity) time interval following start-up of the plant [15]. Peak overshoot (M P ): The maximum deviation attained by the time-response of a plant from its steady-state value with constant input (usually a unit step function) divided by the steady-state value.…”

“…Other applications include mind-driven motion control of mobile [3] and humanoid robots [6], thoughtcontrolled navigation in virtual reality environment [2] and mind-controlled gaming [5]. Its merit lies in the automatic control of external devices without neuromuscular intervention.…”

Applications of Brain–Machine Interface Systems in Stroke Recovery and Rehabilitation

“…The benefits would include increased recruitment of cortical motor networks through motor imagery used to control the BMI, as well as engaging motor learning mechanisms through repetitive training. Researchers have developed and tested a prototypical VR system in healthy individuals that involves controlling a virtual ‘avatar’ using a motor-imagery based BMI [28]. The use of such BMI-based VR rehabilitation in early stages of stroke recovery could significantly alter the trajectory of functional recovery in patients, thereby enhancing quality of life and potentially reducing needs for long-term rehabilitation and associated costs.…”