Motor Intention Decoding During Active and Robot-Assisted Reaching

Robotics rehabilitation is a widely used approach for the treatment of patients with severe motor disabilities, such as stroke survivors. Robots can provide intense, controlled and repeatable rehabilitation and they can also provide different levels of assistance when patients are not able to initiate or complete a movement. Nevertheless, several studies proved that completely passive movements are not sufficient to stimulate neuro-motor recovery and patients' engagement is a key factor for an effective rehabilitation. For this reason it is important to combine techniques for detection of movement intention (MI) with rehabilitation robotics. In this study we developed an algorithm capable of detecting MI before the movement onset, in order to obtain a trigger signal for providing robotics assistance. The proposed algorithm automatically selects the channels used to extract MI based on the motor-information content of each channel. The developed algorithm was tested on data recorded on n = 8 healthy subjects performing 3D reaching movements with an exoskeleton in active and assisted conditions. MI was detected about 400 ms before the beginning of the movement and the performance of the proposed method were significantly higher than the one achieved when six preselected channels, located over motor areas, were used for MI decoding. MI was also detected during robot-assisted movements. Interestingly, in active movements the highest performance was achieved with electrodes over a well-localized cluster above the contralateral and central motor areas, while in passive executions, the areas with the best performances became more sparse.