Decoding of motor imagery movements from EEG signals using SpiNNaker neuromorphic hardware

Abstract

Non-invasive brain machine interfaces (BMIs) on motor imagery movements have been widely studied and used for many years to take advantage of the intuitive link between imagined motor tasks and natural actions. En route to future technical applications of neuromorphic computing, a major current challenge lies in the identification and implementation of brain inspired algorithms to decode recorded signals. Neuromorphic computing is believed to allow real-time implementation of large scale spiking models for processing and computation in non-invasive BMIs. Taking inspiration from the olfactory system of insects, we advance and implement a novel approach to decode and predict imaginary movements from electroencephalogram (EEG) signals. We use a spiking neural network implemented on SpiNNaker (4 chip, 64 cores) neuromorphic hardware. Our work provides a proof of concept for a successful implementation of a functional spiking neural network for decoding two motor imagery (MI) movements on the SpiNNaker system. The approach can be extended to classify more complex MI movements on larger SpiNNaker systems.