EEG-based classification of upper-limb ADL using SNN for active robotic rehabilitation

Repetitive activities of daily living (ADL) and robotic active training are commonly practised in the rehabilitation of paralyzed patients, both of which have been proven rather effective to recover the locomotor function of impaired limbs. ADL classification based on electroencephalogram (EEG) is of great significance to perform active robotic rehabilitation for patients with complete spinal cord injury (SCI) who lose locomotion of affected limbs absolutely, where surface electromyography (sEMG) or active force signal can hardly be detected. It is a challenge to achieve a satisfying result in neuro-rehabilitation robotics using EEG signals due to the high randomness of the EEG data. A classification method is proposed based on spiking neural networks (SNN) to identify the upper-limb ADL of three classes with 14-channel EEG data. The continuous real-number signals are firstly encoded into spike trains through Ben's Spike Algorithm (BSA). The generated spikes are then submitted into a 3-D brain-mapped SNN reservoir called NeuCube trained by Spike Timing Dependant Plasticity (STDP). Spike trains from all neurons of the trained reservoir are finally classified using one version of dynamic evolving spiking neuron networks (deSNN) - deSNNs. Classifications are presented with and without NeuCube respectively on the same EEG data set. Results indicate that using the reservoir improves identification accuracy which turns out pretty promising despite that EEG data is highly noisy, low frequently sampled, and only from 14 channels. The classification technique reveals a great potential for the further implementation of active robotic rehabilitation to the sufferers of complete SCI.