Improved NEuroMOrphic Decoding System for Adaptive and Personalized Neuro-Prosthetics Control

In our previous works we developed a neuromorphic decoder of intended movements of tetraplegic patients using ECoG recordings from brain motor cortex composed by a Motor Control Decoder (MCD) and a Neural Response Decoder (NRD). It was an actor-critic structure able to adapt via reinforcement learning the MCD (actor) based on NRD (critic) predictions. In this paper, we continue the development of novel neuromorphic methods for BMI aiming at further improvement of their functionality. First of all, feature extraction from ECoG data was improved by introduction of additional filtration of raw data. Second, the auto adaptive ability of NRD was upgraded using Intrinsic Plasticity (IP) tuning mechanism. Third, the mechanism of MCD decision improvement using NRD predictions was changed considering labeling approach of NRD training data. The MCD-NRD training and testing was done on a bigger data base and its improved accuracy was demonstrated on new test data sets as well.