Particle filters and beamforming for EEG source estimation

Petia Georgieva; Lyudmila Mihaylova; Nidhal Bouaynaya; Lakhmi Jain

doi:10.1109/IJCNN.2012.6252516

Particle filters and beamforming for EEG source estimation

Petia Georgieva, Lyudmila Mihaylova, Nidhal Bouaynaya, Lakhmi Jain

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

5 Scopus citations

Abstract

This is a proof of concept work that proposes a solution to the inverse problem of EEG source estimation by combining two techniques, namely a Particle Filter (PF) for geometrical (3D) localization of the most active brain zones (expressed by two dipoles) and a beamformer (BF) as a spatial filter for estimation of the oscillations that have originated the recorded EEG data. The estimation is reliable for uncorrelated brain sources.

Original language	English (US)
Title of host publication	2012 International Joint Conference on Neural Networks, IJCNN 2012
DOIs	https://doi.org/10.1109/IJCNN.2012.6252516
State	Published - 2012
Externally published	Yes
Event	2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia Duration: Jun 10 2012 → Jun 15 2012

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Other

Other	2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
Country/Territory	Australia
City	Brisbane, QLD
Period	6/10/12 → 6/15/12

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

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10.1109/IJCNN.2012.6252516

Cite this

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title = "Particle filters and beamforming for EEG source estimation",

abstract = "This is a proof of concept work that proposes a solution to the inverse problem of EEG source estimation by combining two techniques, namely a Particle Filter (PF) for geometrical (3D) localization of the most active brain zones (expressed by two dipoles) and a beamformer (BF) as a spatial filter for estimation of the oscillations that have originated the recorded EEG data. The estimation is reliable for uncorrelated brain sources.",

author = "Petia Georgieva and Lyudmila Mihaylova and Nidhal Bouaynaya and Lakhmi Jain",

year = "2012",

doi = "10.1109/IJCNN.2012.6252516",

language = "English (US)",

isbn = "9781467314909",

series = "Proceedings of the International Joint Conference on Neural Networks",

booktitle = "2012 International Joint Conference on Neural Networks, IJCNN 2012",

note = "2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 ; Conference date: 10-06-2012 Through 15-06-2012",

}

Georgieva, P, Mihaylova, L, Bouaynaya, N & Jain, L 2012, Particle filters and beamforming for EEG source estimation. in 2012 International Joint Conference on Neural Networks, IJCNN 2012., 6252516, Proceedings of the International Joint Conference on Neural Networks, 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, QLD, Australia, 6/10/12. https://doi.org/10.1109/IJCNN.2012.6252516

TY - GEN

T1 - Particle filters and beamforming for EEG source estimation

AU - Georgieva, Petia

AU - Mihaylova, Lyudmila

AU - Bouaynaya, Nidhal

AU - Jain, Lakhmi

PY - 2012

Y1 - 2012

N2 - This is a proof of concept work that proposes a solution to the inverse problem of EEG source estimation by combining two techniques, namely a Particle Filter (PF) for geometrical (3D) localization of the most active brain zones (expressed by two dipoles) and a beamformer (BF) as a spatial filter for estimation of the oscillations that have originated the recorded EEG data. The estimation is reliable for uncorrelated brain sources.

AB - This is a proof of concept work that proposes a solution to the inverse problem of EEG source estimation by combining two techniques, namely a Particle Filter (PF) for geometrical (3D) localization of the most active brain zones (expressed by two dipoles) and a beamformer (BF) as a spatial filter for estimation of the oscillations that have originated the recorded EEG data. The estimation is reliable for uncorrelated brain sources.

UR - http://www.scopus.com/inward/record.url?scp=84865063663&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84865063663&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2012.6252516

DO - 10.1109/IJCNN.2012.6252516

M3 - Conference contribution

AN - SCOPUS:84865063663

SN - 9781467314909

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2012 International Joint Conference on Neural Networks, IJCNN 2012

T2 - 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012

Y2 - 10 June 2012 through 15 June 2012

ER -

Particle filters and beamforming for EEG source estimation

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