Cite
@article{xiong_unsupervised_2018,
abstract = {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 (\textgreater8 dB) and a high spike sorting accuracy (\textgreater90%).},
author = {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.},
doi = {10.1109/TNSRE.2018.2830354},
issn = {1558-0210},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
month = {June},
number = {6},
pages = {1121–1130},
title = {An Unsupervised Compressed Sensing Algorithm for Multi-Channel Neural Recording and Spike Sorting},
volume = {26},
year = {2018}
}
Abstract
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 (textgreater8 dB) and a high spike sorting accuracy (textgreater90%).