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arXiv · 2405.08292

Hybrid Event-Frame Neural Spike Detector for Neuromorphic Implantable BMI

Vivek Mohan·Wee Peng Tay·Arindam Basu
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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.

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BibTeXRIS

Vivek Mohan, Wee Peng Tay, Arindam Basu. 2024-05-14. Hybrid Event-Frame Neural Spike Detector for Neuromorphic Implantable BMI. https://arxiv.org/abs/2405.08292

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