TY - GEN
T1 - EEG sparse source localization via Range Space Rotation
AU - Al Hilli, Ahmed
AU - Najafizadeh, Laleh
AU - Petropulu, Athina
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015
Y1 - 2015
N2 - The problem of sparse Electroencephalography (EEG) source localization can be formulated as a sparse signal recovery problem. However, the dictionary matrix (Lead Field) of a realistic head model has high coherence, indicating that the sparse signal, corresponding to brain activations might not be recoverable via l1-norm minimization techniques. In spite of the high coherence in the EEG dictionary matrix, we can still estimate the support of the underlying source signal as long as the problem satisfies the Range Space Property (RSP). In this paper, we show that one can use an initial estimate of the sparse solution to rotate the range of the sensing matrix transpose and obtain high quality source localization. We derive the conditions which the rotation matrix should meet in order to make the unique least l1-norm solution support match the actual source support. We validate the proposed approach using simulations and a real EEG experiment, and compare the results with those obtained by other methods that have been previously proposed for EEG source localization.
AB - The problem of sparse Electroencephalography (EEG) source localization can be formulated as a sparse signal recovery problem. However, the dictionary matrix (Lead Field) of a realistic head model has high coherence, indicating that the sparse signal, corresponding to brain activations might not be recoverable via l1-norm minimization techniques. In spite of the high coherence in the EEG dictionary matrix, we can still estimate the support of the underlying source signal as long as the problem satisfies the Range Space Property (RSP). In this paper, we show that one can use an initial estimate of the sparse solution to rotate the range of the sensing matrix transpose and obtain high quality source localization. We derive the conditions which the rotation matrix should meet in order to make the unique least l1-norm solution support match the actual source support. We validate the proposed approach using simulations and a real EEG experiment, and compare the results with those obtained by other methods that have been previously proposed for EEG source localization.
UR - http://www.scopus.com/inward/record.url?scp=84963894282&partnerID=8YFLogxK
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U2 - 10.1109/CAMSAP.2015.7383787
DO - 10.1109/CAMSAP.2015.7383787
M3 - Conference contribution
AN - SCOPUS:84963894282
T3 - 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
SP - 265
EP - 268
BT - 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
Y2 - 13 December 2015 through 16 December 2015
ER -