Low-Power FPGA-based Spiking Neural Networks for Real-Time Decoding of Intracortical Neural Activity

Brain-machine interfaces (BMIs) are systems designed to decode neural signals and translate them into commands for external devices. Intracortical microelectrode arrays (MEAs) represent a significant advancement in this field, offering unprecedented spatial and temporal resolutions for monitoring brain activity. However, processing data from MEAs presents challenges due to high data rates and computing power requirements. To address these challenges, we propose a novel solution leveraging spiking neural networks (SNNs) that, due to their similarity to biological neural networks and their event-based nature, promise high compatibility with neural signals and low energy consumption. In this study, we introduce a real-time neural decoding system based on an SNN, deployed on a Lattice iCE40UP5k FPGA. This system is capable of reconstructing multiple target variables, related to the kinematics and kinetics of hand motion, from iEEG signals recorded by a 96-channel MEA. We evaluated the system using two different public datasets, achieving results similar to state-of-the-art neural decoders that use more complex deep learning models. This was obtained while maintaining an average power consumption of 13.9 mW and an average energy consumption per inference of 13.9 uJ.