Combining brain–computer interface and virtual reality for rehabilitation in neurological diseases: A narrative review

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Abstract

<p class="first" id="d15592598e158">The traditional rehabilitation for neurological diseases lacks the active participation of patients, its process is monotonous and tedious, and the effects need to be improved. Therefore, a new type of rehabilitation technology with more active participation combining brain-computer interface (BCI) with virtual reality (VR) has developed rapidly in recent years and has been used in rehabilitation in neurological diseases. </p>

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Brain–computer interfaces for communication and control

Jonathan Wolpaw, Niels Birbaumer, Dennis J McFarland … (2002)

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Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients

Ana R. C. Donati, Solaiman Shokur, Edgard Morya … (2016)

Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in severely paralyzed patients. Yet, no study in animals or in human subjects has indicated that long-term BMI training could induce any type of clinical recovery. Eight chronic (3–13 years) spinal cord injury (SCI) paraplegics were subjected to long-term training (12 months) with a multi-stage BMI-based gait neurorehabilitation paradigm aimed at restoring locomotion. This paradigm combined intense immersive virtual reality training, enriched visual-tactile feedback, and walking with two EEG-controlled robotic actuators, including a custom-designed lower limb exoskeleton capable of delivering tactile feedback to subjects. Following 12 months of training with this paradigm, all eight patients experienced neurological improvements in somatic sensation (pain localization, fine/crude touch, and proprioceptive sensing) in multiple dermatomes. Patients also regained voluntary motor control in key muscles below the SCI level, as measured by EMGs, resulting in marked improvement in their walking index. As a result, 50% of these patients were upgraded to an incomplete paraplegia classification. Neurological recovery was paralleled by the reemergence of lower limb motor imagery at cortical level. We hypothesize that this unprecedented neurological recovery results from both cortical and spinal cord plasticity triggered by long-term BMI usage.

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Rehabilitation for Parkinson's disease: Current outlook and future challenges.

Giovanni Abbruzzese, Roberta Marchese, Laura Avanzino … (2016)

Rehabilitation is considered as an adjuvant to pharmacological and surgical treatments for Parkinson's disease (PD) to maximize functional ability and minimize secondary complications. Originally, approaches were based on empirical experience, but growing evidence suggests that exercise-dependent plasticity constitutes the main mechanism underlying the effects of physiotherapy. Exercise increases synaptic strength and influences neurotransmission, thus potentiating functional circuitry in PD. In addition, exercise is a pivotal element of motor learning. PD patients retain a sufficient capacity of motor learning, though learning rates and performance are reduced in comparison to normal controls. Recent meta-analyses demonstrated that rehabilitation could induce short-lasting, but clinically important benefits, particularly for gait and balance. However, the interventions are largely heterogeneous (stretching, muscle strengthening, balance, postural exercises, occupational therapy, cueing, treadmill training), and there is still no consensus about the optimal approach. Innovative techniques have been recently proposed: virtual reality and exergaming, motor imagery and action observation, robot-assisted physiotherapy and non-conventional therapies (e.g.: dance, martial arts). The rehabilitative program for PD should be "goal-based" (targeted to practicing and learning specific activities in the core areas), but a number of practice variables (intensity, specificity, complexity) need to be identified and the program should tailored to the individual patients' characteristics.