The neuronal sources of EEG: Modeling of simultaneous scalp and intracerebral recordings in epilepsy

Abstract

In many applications which make use of EEG to investigate brain functions, a central question is often to relate the recorded signals to the spatio-temporal organization of the underlying neuronal sources of activity. A modeling attempt to quantitatively investigate this imperfectly known relationship is reported. The proposed plausible model of EEG generation relies on an accurate representation of the neuronal sources of activity. It combines both an anatomically realistic description of the spatial features of the sources (convoluted dipole layer) and a physiologically relevant description of their temporal activities (coupled neuronal populations). The model was used in the particular context of epileptiform activity (interictal spikes) to interpret simultaneously generated scalp and intracerebral EEG. Its integrative properties allowed for the bridging between source-related parameters (spatial extent, location, synchronization) and the properties of resulting EEG signals (amplitude of spikes, amplitude gradient along intracerebral electrodes, topography over scalp electrodes). The sensitivity of both recording modalities to source-related parameters was also studied. The model confirmed that the cortical area involved in interictal spikes is rather large. Its relative location with respect to recording electrodes was found to strongly influence the properties of EEG signals as the source geometry is a critical parameter. The influence, on simulated signals, of the synchronization degree between neuronal populations within the epileptic source was also investigated. The model revealed that intracerebral EEG can reflect epileptic activities corresponding to weak synchronization between neuronal populations of the epileptic patch. These results, as well as the limitations of the model, are discussed.

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

“…To quantitatively evaluate the performance of the four above-mentioned BSS approaches, we simulated 32-channels EEG data, with a spatiotemporal model developed by our team [52][53][54]. In this model, EEG sources were represented as a dipole layer distributed over the cortical surface.…”

ICA-Based EEG denoising: a comparative analysis of fifteen methods

“…The simulated 32-channels EEG data (one observation is displayed in Fig. 1) are generated with a spatio-temporal model developed by our team [33][34][35]. In this model, EEG sources were represented as a dipole layer distributed over the cortical surface.…”

“…Among cumulantbased techniques, representative algorithms of three subfamilies are studied: i) the techniques using only SO statistics of the data such as SOBI [14,15], SOBI rob [20], TFBSS [21], ii) the algorithms based on SO and FO statistics such as JADE [22], CoM 2 [23], and iii) the methods requiring only HO statistics such as ERICA [24], SIMBEC [25], FOBIUM JAD [26,27], ICAR 3 [28,29], FOOBI 1 [30], 4-CANDHAP c [31,32]. Quantitative results are obtained on simulated epileptic data generated with a physiologically-plausible model [33][34][35]. These results are also illustrated on real data recorded in a patient with epilepsy.…”

An Efficient Jacobi-Like Deflationary ICA Algorithm: Application to EEG Denoising

“…The simulated epileptic EEG was generated using a realistic model developped in our team [3]. We built a mesh of the cortical surface from a 3D MRI T1 image of a subject (BrainVisa, SHFJ, Orsay, France).…”

“…From this mesh, P e distributed sources, called "patches", generating interictal spikes, are defined. Each patch is composed of 100 dipole sources to which we assigned hyper-synchronous spikelike activities generated from a model of neuronal populations [3]. From this setup and considering 12 electrodes, namely Fp1, Fp2, C3, C4, T3, T4, O1, O2, F7, F8, T5 and T6, the forward problem was then calculated using a realistic head model made of three nested homogeneous volumes shaping the brain, the skull and the scalp (ASA, ANT, Enschede, Netherlands).…”