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

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 lea...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2018-06, Vol.26 (6), p.1121-1130
Main Authors: Xiong, Tao, Zhang, Jie, Martinez-Rubio, Clarissa, Thakur, Chetan S., Eskandar, Emad N., Chin, Sang Peter, Etienne-Cummings, Ralph, Tran, Trac D.
Format: Article
Language:English
Subjects:
Classification
Clustering
Compressed sensing
Compression
Compression ratio
Dictionaries
dictionary learning
Electrodes
Embedded systems
Embedding
Firing pattern
Integrated circuits
Machine learning
multi-channel
neural recording
Neurons
Recording
Sensors
Sorting
Sorting algorithms
spike sorting
Template matching
Tetrodes
unsupervised
Very large scale integration
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Summary: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%).
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2018.2830354