Analysis of ecog features for movement execution using denoising source separation

Aysegul Gunduz; Justin C. Sanchez; Jose C. Principe

doi:10.1109/MLSP.2008.4685463

Analysis of ecog features for movement execution using denoising source separation

Aysegul Gunduz, Justin C. Sanchez, Jose C. Principe

Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

The major challenge in ECoG-based neuroprosthesis is isolating features in a spectrally and spatially broad range of sources essential for modeling motor behavior. In this study, movement-related spectral modulations are resolved using broadband ECoG recordings passed through a filterbank of constant-Q filters. Denoising source separation is a semiblind source separation methodology which extracts hidden structures of interest within the data by exploiting prior knowledge on the observations. Herein, the methodology is utilized to extract sources that modulate within the frequency content of the hand trajectory. High signal acquisition rates (12kHz) allow for analysis of frequencies beyond the fast gamma oscillations which have been thus far discarded as background activity. Exploratory analysis suggests the first components extracted from envelopes of high spectral bands correlate with the hand trajectory and their spatial distribution covers areas of premotor and primary motor cortices.

Original language	English (US)
Title of host publication	Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Pages	103-108
Number of pages	6
DOIs	https://doi.org/10.1109/MLSP.2008.4685463
State	Published - Dec 1 2008
Event	2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico Duration: Oct 16 2008 → Oct 19 2008

Publication series

Name	Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Other

Other	2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Country	Mexico
City	Cancun
Period	10/16/08 → 10/19/08

ASJC Scopus subject areas

Artificial Intelligence
Software
Electrical and Electronic Engineering

Access to Document

10.1109/MLSP.2008.4685463

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Gunduz, A., Sanchez, J. C., & Principe, J. C. (2008). Analysis of ecog features for movement execution using denoising source separation. In Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 (pp. 103-108). [4685463] (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008). https://doi.org/10.1109/MLSP.2008.4685463

Analysis of ecog features for movement execution using denoising source separation. / Gunduz, Aysegul; Sanchez, Justin C.; Principe, Jose C.

Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008. 2008. p. 103-108 4685463 (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Gunduz, A, Sanchez, JC & Principe, JC 2008, Analysis of ecog features for movement execution using denoising source separation. in Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008., 4685463, Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, pp. 103-108, 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, Cancun, Mexico, 10/16/08. https://doi.org/10.1109/MLSP.2008.4685463

Gunduz A, Sanchez JC, Principe JC. Analysis of ecog features for movement execution using denoising source separation. In Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008. 2008. p. 103-108. 4685463. (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008). https://doi.org/10.1109/MLSP.2008.4685463

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