A 41 μW real-time adaptive neural spike classifier

Abstract

Robust, power- and area-efficient spike classifier, capable of accurate identification of the neural spikes even for low SNR, is a prerequisite for the real-time, implantable, closed-loop brain-machine interface. In this paper, we propose an easily-scalable, 128-channel, programmable, neural spike classifier based on nonlinear energy operator spike… (More)
DOI: 10.1109/BHI.2016.7455941

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Cite this paper

@article{Zjajo2016A4, title={A 41 μW real-time adaptive neural spike classifier}, author={Amir Zjajo and Ren{\'e} van Leuken}, journal={2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)}, year={2016}, pages={489-492} }