Denoising depth EEG signals during DBS using filtering and subspace decomposition

Janis Hofmanis (1) , Olivier Caspary (1) , Valérie Louis-Dorr (1) , Radu Ranta (1) , Louis Maillard (1)

1 CRAN - Centre de Recherche en Automatique de Nancy

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.

Mots clés: Blind source separation (BSS); Stereo-electroencephalography (SEEG); deep brain stimulation (DBS); denoising; electroencephalography (EEG); empirical mode decomposition (EMD); epilepsy; filtering; generalized eigenvalue decomposition (GEVD); singular spectrum analysis (SSA); subspace decomposition

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Neurosciences [q-bio.NC]

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⟩