Publications récentes

SESAME

Systèmes Epileptogènes : SignAux et ModèlEs

 

Quelques publications récentes

(Voir la liste complète dans 'Publications du LTSI' - Consulter également http://www.hal.inserm.fr/LTSI)

mise à jour: 01/2021

1]
M. Arrais, J. Modolo, D. Mogul, and F. Wendling, ‘Design of optimal multi-site brain stimulation protocols via neuro-inspired epilepsy models for abatement of interictal discharges.’, J Neural Eng, Dec. 2020, doi: 10.1088/1741-2552/abd049.
[2]
E. Köksal Ersöz, J. Modolo, F. Bartolomei, and F. Wendling, ‘Neural mass modeling of slow-fast dynamics of seizure initiation and abortion.’, PLoS Comput Biol, vol. 16, no. 11, p. e1008430, Nov. 2020, doi: 10.1371/journal.pcbi.1008430.
[3]
S. Marchesotti, J. Nicolle, I. Merlet, L. H. Arnal, J. P. Donoghue, and A.-L. Giraud, ‘Selective enhancement of low-gamma activity by tACS improves phonemic processing and reading accuracy in dyslexia.’, PLoS Biol, vol. 18, no. 9, p. e3000833, Sep. 2020, doi: 10.1371/journal.pbio.3000833.
[4]
A. Balatskaya et al., ‘The "Connectivity Epileptogenicity Index " (cEI), a method for mapping the different seizure onset patterns in StereoElectroEncephalography recorded seizures.’, Clin Neurophysiol, vol. 131, no. 8, pp. 1947–1955, Aug. 2020, doi: 10.1016/j.clinph.2020.05.029.
[5]
M. Yochum and J. Modolo, ‘NMMGenerator: An automatic neural mass model generator from population graphs.’, J Neural Eng, Jul. 2020, doi: 10.1088/1741-2552/aba799.
[6]
J. Modolo, M. Hassan, G. Ruffini, and A. Legros, ‘Probing the circuits of conscious perception with magnetophosphenes.’, J Neural Eng, vol. 17, no. 3, p. 036034, Jul. 2020, doi: 10.1088/1741-2552/ab97f7.
[7]
M. Al Harrach, H. Mousavi, G. Dieuset, E. Ismailova, and F. Wendling, ‘Model-Guided Design of Microelectrodes for HFO Recording.’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2020, pp. 3428–3431, Jul. 2020, doi: 10.1109/EMBC44109.2020.9176032.
[8]
Y. Denoyer, I. Merlet, F. Wendling, and P. Benquet, ‘Modelling acute and lasting effects of tDCS on epileptic activity.’, J Comput Neurosci, vol. 48, no. 2, pp. 161–176, May 2020, doi: 10.1007/s10827-020-00745-6.
[9]
A. Kabbara, V. Paban, A. Weill, J. Modolo, and M. Hassan, ‘Brain Network Dynamics Correlate with Personality Traits.’, Brain Connect, vol. 10, no. 3, pp. 108–120, Apr. 2020, doi: 10.1089/brain.2019.0723.
[10]
J. Rizkallah, H. Amoud, M. Fraschini, F. Wendling, and M. Hassan, ‘Exploring the Correlation Between M/EEG Source-Space and fMRI Networks at Rest.’, Brain Topogr, vol. 33, no. 2, pp. 151–160, Mar. 2020, doi: 10.1007/s10548-020-00753-w.
[11]
C. Mahjoub, R. Le Bouquin Jeannès, T. Lajnef, and A. Kachouri, ‘Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods’, Biomed Tech (Berl), vol. 65, no. 1, pp. 33–50, Jan. 2020, doi: 10.1515/bmt-2019-0001.
[12]
J. Modolo, M. Hassan, F. Wendling, and P. Benquet, ‘Decoding the circuitry of consciousness: From local microcircuits to brain-scale networks.’, Netw Neurosci, vol. 4, no. 2, pp. 315–337, 2020, doi: 10.1162/netn_a_00119.
[13]
A. Mheich, F. Wendling, and M. Hassan, ‘Brain network similarity: methods and applications.’, Netw Neurosci, vol. 4, no. 3, pp. 507–527, 2020, doi: 10.1162/netn_a_00133.
[14]
M. Kuchenbuch et al., ‘KCNT1 epilepsy with migrating focal seizures shows a temporal sequence with poor outcome, high mortality and SUDEP.’, Brain, vol. 142, no. 10, pp. 2996–3008, Oct. 2019, doi: 10.1093/brain/awz240.
[15]
S. Loiodice et al., ‘Evaluation of seizure liabilities of epileptogenic but non-convulsive agents in rats.’, J Pharmacol Toxicol Methods, vol. 99, p. 106595, Oct. 2019, doi: 10.1016/j.vascn.2019.05.143.
[16]
J.-P. Lefaucheur and F. Wendling, ‘Mechanisms of action of tDCS: A brief and practical overview.’, Neurophysiol Clin, vol. 49, no. 4, pp. 269–275, Sep. 2019, doi: 10.1016/j.neucli.2019.07.013.
[17]
N. Georgi, A. Corvol, and R. Le Bouquin Jeannès, ‘For a More Reliable Measure of Wrist Blood Pressure Using Smartwatch’, Telemed J E Health, vol. 25, no. 9, pp. 862–866, Sep. 2019, doi: 10.1089/tmj.2018.0112.
[18]
J. Duprez et al., ‘Subthalamic nucleus local field potentials recordings reveal subtle effects of promised reward during conflict resolution in Parkinson’s disease.’, Neuroimage, vol. 197, pp. 232–242, Aug. 2019, doi: 10.1016/j.neuroimage.2019.04.071.
[19]
J. Rizkallah, H. Amoud, F. Wendling, and M. Hassan, ‘Effect of connectivity measures on the identification of brain functional core network at rest.’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2019, pp. 6426–6429, Jul. 2019, doi: 10.1109/EMBC.2019.8857331.
[20]
D. Perez et al., ‘Quantification of Neural Conduction Block on the Rat Sciatic Nerve based on EMG Response’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2019, pp. 6450–6453, Jul. 2019, doi: 10.1109/EMBC.2019.8856943.
[21]
M. Arrais, F. Wendling, and J. Modolo, ‘Identification of effective stimulation parameters to abort epileptic seizures in a neural mass model.’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2019, pp. 5208–5211, Jul. 2019, doi: 10.1109/EMBC.2019.8857926.
[22]
A. Carvallo, J. Modolo, P. Benquet, S. Lagarde, F. Bartolomei, and F. Wendling, ‘Biophysical Modeling for Brain Tissue Conductivity Estimation Using SEEG Electrodes.’, IEEE Trans Biomed Eng, vol. 66, no. 6, pp. 1695–1704, Jun. 2019, doi: 10.1109/TBME.2018.2877931.
[23]
J. Aupy, F. Wendling, K. Taylor, J. Bulacio, J. Gonzalez-Martinez, and P. Chauvel, ‘Cortico-striatal synchronization in human focal seizures.’, Brain, vol. 142, no. 5, pp. 1282–1295, May 2019, doi: 10.1093/brain/awz062.
[24]
M. Yochum, J. Modolo, D. J. Mogul, P. Benquet, and F. Wendling, ‘Reconstruction of post-synaptic potentials by reverse modeling of local field potentials.’, J Neural Eng, vol. 16, no. 2, p. 026023, Apr. 2019, doi: 10.1088/1741-2552/aafbfb.
[25]
L. Liu, J. Wu, D. Li, L. Senhadji, and H. Shu, ‘Fractional Wavelet Scattering Network and Applications’, IEEE Trans Biomed Eng, vol. 66, no. 2, pp. 553–563, Feb. 2019, doi: 10.1109/TBME.2018.2850356.
[26]
M. Kuchenbuch et al., ‘Quantitative analysis and EEG markers of KCNT1 epilepsy of infancy with migrating focal seizures.’, Epilepsia, vol. 60, no. 1, pp. 20–32, Jan. 2019, doi: 10.1111/epi.14605.
[27]
J. Rizkallah et al., ‘Decreased integration of EEG source-space networks in disorders of consciousness.’, Neuroimage Clin, vol. 23, p. 101841, 2019, doi: 10.1016/j.nicl.2019.101841.
[28]
V. Paban, J. Modolo, A. Mheich, and M. Hassan, ‘Psychological resilience correlates with EEG source-space brain network flexibility.’, Netw Neurosci, vol. 3, no. 2, pp. 539–550, 2019, doi: 10.1162/netn_a_00079.
[29]
A. Kabbara et al., ‘Detecting modular brain states in rest and task.’, Netw Neurosci, vol. 3, no. 3, pp. 878–901, 2019, doi: 10.1162/netn_a_00090.
[30]
S. Bensaid, J. Modolo, I. Merlet, F. Wendling, and P. Benquet, ‘COALIA: A Computational Model of Human EEG for Consciousness Research.’, Front Syst Neurosci, vol. 13, p. 59, 2019, doi: 10.3389/fnsys.2019.00059.
[31]
J. Modolo, Y. Denoyer, F. Wendling, and P. Benquet, ‘Physiological effects of low-magnitude electric fields on brain activity: advances from in vitro, in vivo and in silico models.’, Curr Opin Biomed Eng, vol. 8, pp. 38–44, Dec. 2018, doi: 10.1016/j.cobme.2018.09.006.
[32]
J. Rizkallah, P. Benquet, A. Kabbara, O. Dufor, F. Wendling, and M. Hassan, ‘Dynamic reshaping of functional brain networks during visual object recognition.’, J Neural Eng, vol. 15, no. 5, p. 056022, Oct. 2018, doi: 10.1088/1741-2552/aad7b1.
[33]
A. Mheich, M. Hassan, M. Khalil, V. Gripon, O. Dufor, and F. Wendling, ‘SimiNet: A Novel Method for Quantifying Brain Network Similarity.’, IEEE Trans Pattern Anal Mach Intell, vol. 40, no. 9, pp. 2238–2249, Sep. 2018, doi: 10.1109/TPAMI.2017.2750160.
[34]
M. Vérin and P. Benquet, ‘The move: When neurosciences teach us to better teach neurosciences.’, J Neurol Sci, vol. 391, pp. 149–150, Aug. 2018, doi: 10.1016/j.jns.2018.06.002.
[35]
M. Shamas et al., ‘On the origin of epileptic High Frequency Oscillations observed on clinical electrodes.’, Clin Neurophysiol, vol. 129, no. 4, pp. 829–841, Apr. 2018, doi: 10.1016/j.clinph.2018.01.062.
[36]
A. Kabbara, H. Eid, W. El Falou, M. Khalil, F. Wendling, and M. Hassan, ‘Reduced integration and improved segregation of functional brain networks in Alzheimer’s disease.’, J Neural Eng, vol. 15, no. 2, p. 026023, Apr. 2018, doi: 10.1088/1741-2552/aaaa76.
[37]
A. I. Hernández et al., ‘Kinesthetic stimulation for obstructive sleep apnea syndrome: An “on-off” proof of concept trial’, Sci Rep, vol. 8, no. 1, p. 3092, Feb. 2018, doi: 10.1038/s41598-018-21430-w.
[38]
I. Lambert et al., ‘Brain regions and epileptogenicity influence epileptic interictal spike production and propagation during NREM sleep in comparison with wakefulness.’, Epilepsia, vol. 59, no. 1, pp. 235–243, Jan. 2018, doi: 10.1111/epi.13958.
[39]
S. Davarpanah Jazi, J. Modolo, C. Baker, S. Villard, and A. Legros, ‘Effects of A 60 Hz Magnetic Field of Up to 50 milliTesla on Human Tremor and EEG: A Pilot Study.’, Int J Environ Res Public Health, vol. 14, no. 12, Nov. 2017, doi: 10.3390/ijerph14121446.
[40]
B. Ridley et al., ‘Simultaneous Intracranial EEG-fMRI Shows Inter-Modality Correlation in Time-Resolved Connectivity Within Normal Areas but Not Within Epileptic Regions.’, Brain Topogr, vol. 30, no. 5, pp. 639–655, Sep. 2017, doi: 10.1007/s10548-017-0551-5.
[41]
J. Modolo, A. W. Thomas, and A. Legros, ‘Human exposure to power frequency magnetic fields up to 7.6 mT: An integrated EEG/fMRI study.’, Bioelectromagnetics, vol. 38, no. 6, pp. 425–435, Sep. 2017, doi: 10.1002/bem.22064.
[42]
N. Jrad et al., Automatic Detection and Classification of High-Frequency Oscillations in Depth-EEG Signals., vol. 64, no. 9. United States, 2017.
[43]
H. Becker et al., ‘SISSY: An efficient and automatic algorithm for the analysis of EEG sources based on structured sparsity.’, Neuroimage, vol. 157, pp. 157–172, Aug. 2017, doi: 10.1016/j.neuroimage.2017.05.046.
[44]
P. Jiruska et al., ‘Update on the mechanisms and roles of high-frequency oscillations in seizures and epileptic disorders.’, Epilepsia, vol. 58, no. 8, pp. 1330–1339, Aug. 2017, doi: 10.1111/epi.13830.
[45]
M. Saleh, A. Karfoul, A. Kachenoura, L. Senhadji, and L. Albera, ‘Reliable gradient search directions for kurtosis-based deflationary ICA: Application to physiological signal processing.’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2017, pp. 2790–2793, Jul. 2017, doi: 10.1109/EMBC.2017.8037436.
[46]
L. Hamid et al., ‘Spatial projection as a preprocessing step for EEG source reconstruction using spatiotemporal Kalman filtering.’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2017, pp. 2213–2217, Jul. 2017, doi: 10.1109/EMBC.2017.8037294.
[47]
L. Hamid et al., ‘Source reconstruction via the spatiotemporal Kalman filter and LORETA from EEG time series with 32 or fewer electrodes.’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2017, pp. 2218–2222, Jul. 2017, doi: 10.1109/EMBC.2017.8037295.
[48]
F. Bartolomei et al., ‘Defining epileptogenic networks: Contribution of SEEG and signal analysis.’, Epilepsia, vol. 58, no. 7, pp. 1131–1147, Jul. 2017, doi: 10.1111/epi.13791.
[49]
A. Kabbara, W. El Falou, M. Khalil, F. Wendling, and M. Hassan, ‘The dynamic functional core network of the human brain at rest.’, Sci Rep, vol. 7, no. 1, p. 2936, Jun. 2017, doi: 10.1038/s41598-017-03420-6.
[50]
F. Mina et al., ‘Model-guided control of hippocampal discharges by local direct current stimulation.’, Sci Rep, vol. 7, no. 1, p. 1708, May 2017, doi: 10.1038/s41598-017-01867-1.
[51]
W. Xiang, A. Karfoul, H. Shu, and R. Le Bouquin Jeannès, ‘A local adjustment strategy for the initialization of dynamic causal modelling to infer effective connectivity in brain epileptic structures’, Comput Biol Med, vol. 84, pp. 30–44, May 2017, doi: 10.1016/j.compbiomed.2017.03.006.
[52]
M. Hassan et al., ‘Identification of Interictal Epileptic Networks from Dense-EEG.’, Brain Topogr, vol. 30, no. 1, pp. 60–76, Jan. 2017, doi: 10.1007/s10548-016-0517-z.
[53]
N. Roehri, F. Pizzo, F. Bartolomei, F. Wendling, and C.-G. Bénar, ‘What are the assets and weaknesses of HFO detectors? A benchmark framework based on realistic simulations.’, PLoS One, vol. 12, no. 4, p. e0174702, 2017, doi: 10.1371/journal.pone.0174702.
[54]
M. Hassan et al., ‘Functional connectivity disruptions correlate with cognitive phenotypes in Parkinson’s disease.’, Neuroimage Clin, vol. 14, pp. 591–601, 2017, doi: 10.1016/j.nicl.2017.03.002.
[55]
F. Wendling et al., ‘Brain (Hyper)Excitability Revealed by Optimal Electrical Stimulation of GABAergic Interneurons.’, Brain Stimul, vol. 9, no. 6, pp. 919–932, Dec. 2016, doi: 10.1016/j.brs.2016.07.001.
[56]
R. A. Chowdhury et al., ‘Complex patterns of spatially extended generators of epileptic activity: Comparison of source localization methods cMEM and 4-ExSo-MUSIC on high resolution EEG and MEG  data.’, Neuroimage, vol. 143, pp. 175–195, Dec. 2016, doi: 10.1016/j.neuroimage.2016.08.044.
[57]
P. Kurbatova et al., ‘Dynamic changes of depolarizing GABA in a computational model of epileptogenic brain: Insight for Dravet syndrome.’, Exp Neurol, vol. 283, no. Pt A, pp. 57–72, Sep. 2016, doi: 10.1016/j.expneurol.2016.05.037.
[58]
S. Aubert et al., ‘The role of sub-hippocampal versus hippocampal regions in bitemporal lobe epilepsies.’, Clin Neurophysiol, vol. 127, no. 9, pp. 2992–2999, Sep. 2016, doi: 10.1016/j.clinph.2016.06.021.
[59]
W. Xiang, C. Yang, A. Karfoul, and R. Le Bouquin Jeannes, ‘Quantifying connectivity in a physiology based model using adaptive dynamic causal modelling’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2016, pp. 2818–2821, Aug. 2016, doi: 10.1109/EMBC.2016.7591316.
[60]
M. Shamas, P. Benquet, I. Merlet, W. El Falou, M. Khalil, and F. Wendling, ‘Computational modeling of high frequency oscillations recorded with clinical intracranial macroelectrodes.’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2016, pp. 1014–1017, Aug. 2016, doi: 10.1109/EMBC.2016.7590874.
[61]
M. Saleh, A. Karfoul, A. Kachenoura, L. Senhadji, and L. Albera, ‘Low cost and efficient kurtosis-based deflationary ICA method: application to MRS sources separation problem.’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2016, pp. 3191–3194, Aug. 2016, doi: 10.1109/EMBC.2016.7591407.
[62]
Y. Dietrich et al., ‘Structural and functional changes during epileptogenesis in the mouse model of medial temporal lobe epilepsy.’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2016, pp. 4005–4008, Aug. 2016, doi: 10.1109/EMBC.2016.7591605.
[63]
S. Gataullina et al., ‘Epilepsy in young Tsc1(+/-) mice exhibits age-dependent expression that mimics that of human tuberous sclerosis complex.’, Epilepsia, vol. 57, no. 4, pp. 648–659, Apr. 2016, doi: 10.1111/epi.13325.
[64]
F. Wendling, P. Benquet, F. Bartolomei, and V. Jirsa, ‘Computational models of epileptiform activity.’, J Neurosci Methods, vol. 260, pp. 233–251, Feb. 2016, doi: 10.1016/j.jneumeth.2015.03.027.
[65]
F. Bonini, I. Lambert, F. Wendling, A. McGonigal, and F. Bartolomei, ‘Altered synchrony and loss of consciousness during frontal lobe seizures.’, Clin Neurophysiol, vol. 127, no. 2, pp. 1170–1175, Feb. 2016, doi: 10.1016/j.clinph.2015.04.050.
[66]
F. Bartolomei et al., ‘What is the concordance between the seizure onset zone and the irritative zone? A SEEG quantified study.’, Clin Neurophysiol, vol. 127, no. 2, pp. 1157–1162, Feb. 2016, doi: 10.1016/j.clinph.2015.10.029.
[67]
S. Blanchard et al., ‘A New Computational Model for Neuro-Glio-Vascular Coupling: Astrocyte Activation Can Explain Cerebral Blood Flow Nonlinear Response to Interictal Events.’, PLoS One, vol. 11, no. 2, p. e0147292, 2016, doi: 10.1371/journal.pone.0147292.
[68]
M. Hassan, P. Benquet, A. Biraben, C. Berrou, O. Dufor, and F. Wendling, ‘Dynamic reorganization of functional brain networks during picture naming.’, Cortex, vol. 73, pp. 276–288, Dec. 2015, doi: 10.1016/j.cortex.2015.08.019.
[69]
R. G. Andrzejak et al., ‘Localization of Epileptogenic Zone on Pre-surgical Intracranial EEG Recordings: Toward a Validation of Quantitative Signal Analysis Approaches.’, Brain Topogr, vol. 28, no. 6, pp. 832–837, Nov. 2015, doi: 10.1007/s10548-014-0380-8.
[70]
N. Jrad, A. Kachenoura, I. Merlet, A. Nica, C. G. Benar, and F. Wendling, ‘Classification of high frequency oscillations in epileptic intracerebral EEG.’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2015, pp. 574–577, Aug. 2015, doi: 10.1109/EMBC.2015.7318427.
[71]
M. Hassan, A. Mheich, A. Biraben, I. Merlet, and F. Wendling, ‘Identification of epileptogenic networks from dense EEG: A model-based study.’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2015, pp. 5610–5613, Aug. 2015, doi: 10.1109/EMBC.2015.7319664.
[72]
A. Fargeas et al., ‘A new parameter computed with independent component analysis to predict rectal toxicity following prostate cancer radiotherapy.’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2015, pp. 2657–2660, Aug. 2015, doi: 10.1109/EMBC.2015.7318938.
[73]
V. Nagaraj et al., ‘Future of seizure prediction and intervention: closing the loop.’, J Clin Neurophysiol, vol. 32, no. 3, pp. 194–206, Jun. 2015, doi: 10.1097/WNP.0000000000000139.
[74]
L. Kuhlmann, D. B. Grayden, F. Wendling, and S. J. Schiff, ‘Role of multiple-scale modeling of epilepsy in seizure forecasting.’, J Clin Neurophysiol, vol. 32, no. 3, pp. 220–226, Jun. 2015, doi: 10.1097/WNP.0000000000000149.
[75]
S. Hajipour Sardouie, M. Bagher Shamsollahi, L. Albera, and I. Merlet, ‘Denoising of Ictal EEG Data Using Semi-Blind Source Separation Methods Based on Time-Frequency Priors.’, IEEE J Biomed Health Inform, vol. 19, no. 3, pp. 839–847, May 2015, doi: 10.1109/JBHI.2014.2336797.
[76]
J. Coloigner et al., ‘A Novel Classification Method for Prediction of Rectal Bleeding in Prostate Cancer Radiotherapy Based on a Semi-Nonnegative ICA of 3D Planned Dose Distributions.’, IEEE J Biomed Health Inform, vol. 19, no. 3, pp. 1168–1177, May 2015, doi: 10.1109/JBHI.2014.2328315.
[77]
A. Mheich, M. Hassan, M. Khalil, C. Berrou, and F. Wendling, ‘A new algorithm for spatiotemporal analysis of brain functional connectivity.’, J Neurosci Methods, vol. 242, pp. 77–81, Mar. 2015, doi: 10.1016/j.jneumeth.2015.01.002.
[78]
A. Fargeas et al., ‘On feature extraction and classification in prostate cancer radiotherapy using tensor decompositions.’, Med Eng Phys, vol. 37, no. 1, pp. 126–131, Jan. 2015, doi: 10.1016/j.medengphy.2014.08.009.
[79]
N. Taheri et al., ‘Feasibility of blind source separation methods for the denoising of dense-array EEG.’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2015, pp. 4773–4776, 2015, doi: 10.1109/EMBC.2015.7319461.
[80]
A. Legros, J. Modolo, S. Brown, J. Roberston, and A. W. Thomas, ‘Effects of a 60 Hz Magnetic Field Exposure Up to 3000 μT on Human Brain Activation as Measured by Functional Magnetic Resonance Imaging.’, PLoS One, vol. 10, no. 7, p. e0132024, 2015, doi: 10.1371/journal.pone.0132024.
[81]
M. Hassan, M. Shamas, M. Khalil, W. El Falou, and F. Wendling, ‘EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome.’, PLoS One, vol. 10, no. 9, p. e0138297, 2015, doi: 10.1371/journal.pone.0138297.
[82]
L. Albera et al., ‘Localization of spatially distributed brain sources after a tensor-based preprocessing of interictal epileptic EEG data.’, Annu Int Conf IEEE Eng Med Biol Soc, vol. 2015, pp. 6995–6998, 2015, doi: 10.1109/EMBC.2015.7320002