Abstract
Towards non-invasive neuroprosthetic systems for motor control, electrocorticogram (ECoG) recordings provide an intermediate step from microwire single neuron recordings to electroencephalograms. Preprocessing modalities that emphasize amplitude modulation and temporal power in the ECoG are employed to build human brain-machine interfaces, which have been previously shown to modulate with hand behavior by means of linear filters, though, with limited accuracy. We improve the online decoding performance of the amplitude modulation across the recording spectra by employing echo state networks (ESNs) which provide nonlinear mappings without compromising training complexity and filter order compared to basic linear filters and other neural networks. Preliminary results show an increase of 15% in the average correlation of ESN outputs with actual hand trajectories compared to linear mappings.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering |
| Pages | 514-517 |
| Number of pages | 4 |
| DOIs | |
| State | Published - Sep 25 2007 |
| Externally published | Yes |
| Event | 3rd International IEEE EMBS Conference on Neural Engineering - Kohala Coast, HI, United States Duration: May 2 2007 → May 5 2007 |
Other
| Other | 3rd International IEEE EMBS Conference on Neural Engineering |
|---|---|
| Country | United States |
| City | Kohala Coast, HI |
| Period | 5/2/07 → 5/5/07 |
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ASJC Scopus subject areas
- Biotechnology
- Bioengineering
- Neuroscience (miscellaneous)
Cite this
Echo state networks for motor control of human ECoG neuroprosthetics. / Gunduz, Aysegul; Ozturk, Mustafa C.; Sanchez, Justin C.; Principe, Jose C.
Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering. 2007. p. 514-517 4227327.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Echo state networks for motor control of human ECoG neuroprosthetics
AU - Gunduz, Aysegul
AU - Ozturk, Mustafa C.
AU - Sanchez, Justin C.
AU - Principe, Jose C.
PY - 2007/9/25
Y1 - 2007/9/25
N2 - Towards non-invasive neuroprosthetic systems for motor control, electrocorticogram (ECoG) recordings provide an intermediate step from microwire single neuron recordings to electroencephalograms. Preprocessing modalities that emphasize amplitude modulation and temporal power in the ECoG are employed to build human brain-machine interfaces, which have been previously shown to modulate with hand behavior by means of linear filters, though, with limited accuracy. We improve the online decoding performance of the amplitude modulation across the recording spectra by employing echo state networks (ESNs) which provide nonlinear mappings without compromising training complexity and filter order compared to basic linear filters and other neural networks. Preliminary results show an increase of 15% in the average correlation of ESN outputs with actual hand trajectories compared to linear mappings.
AB - Towards non-invasive neuroprosthetic systems for motor control, electrocorticogram (ECoG) recordings provide an intermediate step from microwire single neuron recordings to electroencephalograms. Preprocessing modalities that emphasize amplitude modulation and temporal power in the ECoG are employed to build human brain-machine interfaces, which have been previously shown to modulate with hand behavior by means of linear filters, though, with limited accuracy. We improve the online decoding performance of the amplitude modulation across the recording spectra by employing echo state networks (ESNs) which provide nonlinear mappings without compromising training complexity and filter order compared to basic linear filters and other neural networks. Preliminary results show an increase of 15% in the average correlation of ESN outputs with actual hand trajectories compared to linear mappings.
UR - http://www.scopus.com/inward/record.url?scp=34548805856&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34548805856&partnerID=8YFLogxK
U2 - 10.1109/CNE.2007.369722
DO - 10.1109/CNE.2007.369722
M3 - Conference contribution
SN - 1424407923
SN - 9781424407927
SP - 514
EP - 517
BT - Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering
ER -