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 |
|---|---|
| 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 | |
| State | Published - Dec 1 2008 |
| Externally published | Yes |
| Event | 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico Duration: Oct 16 2008 → Oct 19 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 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Electrical and Electronic Engineering
Cite this
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.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Analysis of ecog features for movement execution using denoising source separation
AU - Gunduz, Aysegul
AU - Sanchez, Justin C.
AU - Principe, Jose C.
PY - 2008/12/1
Y1 - 2008/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=58049159579&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58049159579&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2008.4685463
DO - 10.1109/MLSP.2008.4685463
M3 - Conference contribution
SN - 9781424423767
SP - 103
EP - 108
BT - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
ER -