Denoising depth EEG signals during DBS using filtering and subspace decomposition - Archive ouverte HAL
Article Dans Une Revue IEEE Transactions on Biomedical Engineering Année : 2013

Denoising depth EEG signals during DBS using filtering and subspace decomposition

Résumé

In difficult epileptic patients, the brain structures are explored by means of depth multicontact electrodes (Stereo-ElectroEncephaloGraphy, SEEG). Recently, a novel diagnostic technique allows an accurate definition of the epileptogenic zone using Deep Brain Stimulation (DBS). The stimulation signal propagates in the brain and thus it appears on most of the other SEEG electrodes, masking the local brain electro-physiological activity. The objective of this paper is the DBS-SEEG signals detrending and denoising in order to recover the masked physiological sources. We review the main filtering methods and put forward an approach based on the combination of filtering with Generalized Eigenvalue Decomposition (GEVD). An experimental study on simulated and real SEEG shows that our approach is able to separate DBS sources from brain activity. The best results are obtained by an original SSA-GEVD approach.
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Dates et versions

hal-00853707 , version 1 (07-04-2022)

Identifiants

Citer

Janis Hofmanis, Olivier Caspary, Valérie Louis-Dorr, Radu Ranta, Louis Maillard. Denoising depth EEG signals during DBS using filtering and subspace decomposition. IEEE Transactions on Biomedical Engineering, 2013, 60 (10), pp.2686-2695. ⟨10.1109/TBME.2013.2262212⟩. ⟨hal-00853707⟩
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