Strategies for high-performance resource-efficient compression of neural spike recordings.
Thorbergsson, Palmi Thor LU ; Garwicz, Martin LU ; Schouenborg, Jens LU
and Johansson, Anders J
LU
(2014)
In PLoS ONE
9(4).
- Abstract
- Brain-machine interfaces (BMIs) based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve... (More)
- Brain-machine interfaces (BMIs) based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve spike detection, spike alignment and spike compression. Performance is analyzed in terms of spike reconstruction and spike sorting performance after wireless transmission of the compressed spike waveforms. Compression is performed with transform coding, using five different compression bases, one of which we pay special attention to. That basis is a fixed basis derived, by singular value decomposition, from a large assembly of experimentally obtained spike waveforms, and therefore represents a generic basis specially suitable for compressing spike waveforms. Our results show that a compression factor of 99.8%, compared to transmitting the raw acquired data, can be achieved using the fixed generic compression basis without compromising performance in spike reconstruction and spike sorting. Besides illustrating the relative performances of various system architectures and compression bases, our findings show that compression of spikes with a fixed generic compression basis derived from spike data provides better performance than compression with downsampling or the Haar basis, given that no optimization procedures are implemented for compression coefficients, and the performance is similar to that obtained when the optimal SVD based basis is used. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4430188
- author
- Thorbergsson, Palmi Thor
LU
; Garwicz, Martin
LU
; Schouenborg, Jens
LU
and Johansson, Anders J
LU
- organization
- publishing date
- 2014
- type
- Contribution to journal
- publication status
- published
- subject
- in
- PLoS ONE
- volume
- 9
- issue
- 4
- article number
- e93779
- publisher
- Public Library of Science (PLoS)
- external identifiers
-
- pmid:24727834
- wos:000336736200026
- scopus:84899629257
- pmid:24727834
- ISSN
- 1932-6203
- DOI
- 10.1371/journal.pone.0093779
- language
- English
- LU publication?
- yes
- id
- 2e48298b-c20b-405c-9ad1-42cf559f99c1 (old id 4430188)
- alternative location
- http://www.ncbi.nlm.nih.gov/pubmed/24727834?dopt=Abstract
- date added to LUP
- 2016-04-01 13:22:54
- date last changed
- 2024-04-16 02:43:55
@article{2e48298b-c20b-405c-9ad1-42cf559f99c1,
abstract = {{Brain-machine interfaces (BMIs) based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve spike detection, spike alignment and spike compression. Performance is analyzed in terms of spike reconstruction and spike sorting performance after wireless transmission of the compressed spike waveforms. Compression is performed with transform coding, using five different compression bases, one of which we pay special attention to. That basis is a fixed basis derived, by singular value decomposition, from a large assembly of experimentally obtained spike waveforms, and therefore represents a generic basis specially suitable for compressing spike waveforms. Our results show that a compression factor of 99.8%, compared to transmitting the raw acquired data, can be achieved using the fixed generic compression basis without compromising performance in spike reconstruction and spike sorting. Besides illustrating the relative performances of various system architectures and compression bases, our findings show that compression of spikes with a fixed generic compression basis derived from spike data provides better performance than compression with downsampling or the Haar basis, given that no optimization procedures are implemented for compression coefficients, and the performance is similar to that obtained when the optimal SVD based basis is used.}},
author = {{Thorbergsson, Palmi Thor and Garwicz, Martin and Schouenborg, Jens and Johansson, Anders J}},
issn = {{1932-6203}},
language = {{eng}},
number = {{4}},
publisher = {{Public Library of Science (PLoS)}},
series = {{PLoS ONE}},
title = {{Strategies for high-performance resource-efficient compression of neural spike recordings.}},
url = {{https://lup.lub.lu.se/search/files/3337359/4882719.pdf}},
doi = {{10.1371/journal.pone.0093779}},
volume = {{9}},
year = {{2014}},
}