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An Unsupervised Compressed Sensing Algorithm for Multi-Channel Neural Recording and Spike Sorting

Xiong, Tao and Zhang, Jie and Martinez-Rubio, Clarissa and Thakur, Chetan S and Eskandar, Emad N and Chin, Sang Peter and Etienne-Cummings, Ralph and Tran, Trac D (2018) An Unsupervised Compressed Sensing Algorithm for Multi-Channel Neural Recording and Spike Sorting. In: IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 26 (6). pp. 1121-1130.

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Official URL: https://dx.doi.org/10.1109/TNSRE.2018.2830354


We propose an unsupervised compressed sensing (CS)-based framework to compress, recover, and cluster neural action potentials. This framework can be easily integrated into high-density multi-electrode neural recording VLSI systems. Embedding spectral clustering and group structures in dictionary learning, we extend the proposed framework to unsupervised spike sorting without prior label information. Additionally, we incorporate group sparsity concepts in the dictionary learning to enable the framework for multi-channel neural recordings, as in tetrodes. To further improve spike sorting success rates in the CS framework, we embed template matching in sparse coding to jointly predict clusters of spikes. Our experimental results demonstrate that the proposed CS-based framework can achieve a high compression ratio (8: 1 to 20: 1), with a high quality reconstruction performance (>8 dB) and a high spike sorting accuracy (>90%).

Item Type: Journal Article
Additional Information: Copyright of this article belong to IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Depositing User: Id for Latest eprints
Date Deposited: 02 Aug 2018 15:47
Last Modified: 02 Aug 2018 15:47
URI: http://eprints.iisc.ac.in/id/eprint/60350

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