TY - GEN
T1 - Statistical approach for reconstruction of dynamic brain dipoles based on EEG data
AU - Georgieva, Petia
AU - Silva, Filipe
AU - Mihaylova, Lyudmila
AU - Bouaynaya, Nidhal
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/3
Y1 - 2014/9/3
N2 - In this paper, we propose a statistical approach to reconstruct the brain neuronal activity based only on recorded EEG data. The brain zones with the strongest activity are expressed at a macro level by a few number of active brain dipoles. Normally, for solving the EEG inverse problem, fixed dipole locations are assumed, independently of the different stimuli that excite the brain. The proposed particle filter (PF) framework presents a shift in the current paradigm by estimating dynamic brain dipoles, which may vary from one location to another in the brain depending on internal/external stimuli that may affect the brain. Also, in contrast to previous solutions, the proposed PF algorithm estimates simultaneously, the number of the active dipoles, their moving locations and their respective oscillations in the three dimensional head geometry.
AB - In this paper, we propose a statistical approach to reconstruct the brain neuronal activity based only on recorded EEG data. The brain zones with the strongest activity are expressed at a macro level by a few number of active brain dipoles. Normally, for solving the EEG inverse problem, fixed dipole locations are assumed, independently of the different stimuli that excite the brain. The proposed particle filter (PF) framework presents a shift in the current paradigm by estimating dynamic brain dipoles, which may vary from one location to another in the brain depending on internal/external stimuli that may affect the brain. Also, in contrast to previous solutions, the proposed PF algorithm estimates simultaneously, the number of the active dipoles, their moving locations and their respective oscillations in the three dimensional head geometry.
UR - http://www.scopus.com/inward/record.url?scp=84908491264&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84908491264&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2014.6889663
DO - 10.1109/IJCNN.2014.6889663
M3 - Conference contribution
AN - SCOPUS:84908491264
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 2592
EP - 2599
BT - Proceedings of the International Joint Conference on Neural Networks
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Joint Conference on Neural Networks, IJCNN 2014
Y2 - 6 July 2014 through 11 July 2014
ER -