Revisión sobre nuevos enfoques de terapias de neurorrehabilitación para pacientes con trastornos neurológicos mediante dispositivos de pedaleo

Introduction

Currently there are several neurorehabilitation therapies for people with motor impairments caused by neurological disorders. Therapies based on pedaling devices have stood out for their advantages for the recovery of motor function, the prevention or treatment for respiratory and cardiovascular complications, and the induction of neuroplasticity. The Brain Computer Interface (BCI) systems constitute new approaches to provide a rehabilitation that actively involves the patient in therapy and is capable of controlling robotic devices such as stationary bicycles or motorized pedals with their own neural signals.

Aim

Reflect the current state of BCI systems that use pedaling devices for the therapy of patients with neurological disorders.

Methods

A systematic review was carried out in the PubMed and Science Direct databases and other sources such as IEEEXplore, Sensors magazine and Springer publishing house. Original studies and case reports from the last 10 years based on ICC systems for classification of imaginary pedaling movement from EEG signals were included; in healthy subjects and patients with neurological diseases. Papers that did not show quantitative results from metrics such as accuracy during evaluation were excluded. Risk of bias was assessed using the Critical Appraisal Skills Program (CASP).

Results

8 studies that met all eligibility requirements were included. The population of healthy subjects was 43, compared to 6 patients with cerebrovascular accident (CVA) and spinal cord injury (SCI). One of the current limitations of community ICC studies is the scarcity of subjects for the evaluation of different methodologies. The investigations not only yielded quantitative results regarding the performance of the implemented computational algorithms, but also the cortical effects they have produced during cycling as a rehabilitation therapy.

Discussion

Various authors have shown that treatment with pedaling devices modulates brain and muscle activity. Hence, there are patterns or characteristics during real or imagined pedaling movements. From this information, which can be extracted from surface myoelectric signals (sEMG) or electroencephalography (EEG), it is possible to detect pedaling movement intention, and to control devices for these purposes, many of which are already available in the market.

Conclusion

Research about BCI systems based on the detection of imaginary pedaling movement is recent and has achieved a remarkable performance in classification. Commercial devices have yet to incorporate the use of motor imagery therapy.