Hybrid Event-Frame Neural Spike Detector for Neuromorphic Implantable BMI

TL;DR

This paper presents two innovative neural spike detection methods for neuromorphic brain-machine interfaces, demonstrating high accuracy and low false detections in synthetic and real data, advancing implantable neural decoding technology.

Contribution

Introduces two novel spike detection schemes, one event-based and one neural network-based, optimized for in-vivo and off-implant applications in neuromorphic iBMIs.

Findings

Event-based detector achieves 94-97% accuracy.

Neural network-based detector achieves 96-99% accuracy.

Both methods outperform traditional detection approaches.

Abstract

This work introduces two novel neural spike detection schemes intended for use in next-generation neuromorphic brain-machine interfaces (iBMIs). The first, an Event-based Spike Detector (Ev-SPD) which examines the temporal neighborhood of a neural event for spike detection, is designed for in-vivo processing and offers high sensitivity and decent accuracy (94-97%). The second, Neural Network-based Spike Detector (NN-SPD) which operates on hybrid temporal event frames, provides an off-implant solution using shallow neural networks with impressive detection accuracy (96-99%) and minimal false detections. These methods are evaluated using a synthetic dataset with varying noise levels and validated through comparison with ground truth data. The results highlight their potential in next-gen neuromorphic iBMI systems and emphasize the need to explore this direction further to understand their resource-efficient and high-performance capabilities for practical iBMI settings.