eSPANNet: Evolving Spike Pattern Association Neural Network for Spike-based Supervised Incremental Learning and Its Application for Single-trial Brain Computer Interfaces

Objective: Due to the non-stationarity and high trialto-trial variability, online event prediction from biomedical signals is challenging. This is more significant when it is applied to neurological rehabilitation where the person incrementally learns to regain the control of movement. eSPANNet is a computational model inspired by the incremental learning for motor control in living nervous systems. It is inspired by the concept of 'population vectors' which have been experimentally proven by several computational neuroscience studies. In this paper, we present a proof-of-concept study on the proposed computational model. Our goal is to utilize the polychronization effect of Spiking Neural Networks to develop a better neural decoder for Brain-Computer Interfaces.
Methods: The eSPANNet model contains a network of Spike Pattern Association Neurons, a spiking neuron model which is able to emit spikes at the desired time-point.
Results: The proposed approach was experimentally validated using the finger flexion prediction dataset from the fourth BCI competition. The results show that eSPANNet results in 1) a higher classification accuracy, sensitivity and F1 score compared to several other multi-class classifiers and, 2) a better approximation of the actual movement compared to several regression analysis based approaches.
Conclusion and Significance: The novelty of our algorithm is the ability to learn which inputs to focus on in an online manner. We suggest that the eSPANNet is a better BCI decoder due to its i) incremental and life-long learning, ii) compatibility with the neuromorphic platforms and, iii) ability to address the non-stationarity of brain data.