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Electrical Engineering and Systems Science > Signal Processing

arXiv:2405.08292 (eess)
[Submitted on 14 May 2024]

Title:Hybrid Event-Frame Neural Spike Detector for Neuromorphic Implantable BMI

Authors: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.
Comments: This paper has been accepted for 2024 IEEE International Symposium on Circuits and Systems (ISCAS), Singapore
Subjects: Signal Processing (eess.SP)
Cite as: arXiv:2405.08292 [eess.SP]
  (or arXiv:2405.08292v1 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.2405.08292

Submission history

From: Vivek Mohan [view email]
[v1] Tue, 14 May 2024 03:27:09 UTC (1,930 KB)
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