Topography-Time-Frequency Atomic Decomposition for Event-Related M/EEG Signals.

International audienceWe present a method for decomposing MEG or EEG data (channel x time x trials) into a set of atoms with fixed spatial and time-frequency signatures. The spatial part (i.e., topography) is obtained by independent component analysis (ICA). We propose a frequency prewhitening procedure as a pre-processing step before ICA, which gives access to high frequency activity. The time-frequency part is obtained with a novel iterative procedure, which is an extension of the matching pursuit procedure. The method is evaluated on a simulated dataset presenting both low-frequency evoked potentials and high-frequency oscillatory activity. We show that the method is able to recover well both low-frequency and high-frequency simulated activities. There was however cross-talk across some recovered components due to the correlation introduced in the simulation

Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals

Dictionary Learning has proven to be a powerful tool for many image processing tasks, where atoms are typically defined on small image patches. As a drawback, the dictionary only encodes basic structures. In addition, this approach treats patches of different locations in one single set, which means a loss of information when features are well-aligned across signals. This is the case, for instance, in multi-trial magneto- or electroencephalography (M/EEG). Learning the dictionary on the entire signals could make use of the alignement and reveal higher-level features. In this case, however, small missalignements or phase variations of features would not be compensated for. In this paper, we propose an extension to the common dictionary learning framework to overcome these limitations by allowing atoms to adapt their position across signals. The method is validated on simulated and real neuroelectric data.Comment: 9 pages, 5 figures, minor correction

Consensus Matching Pursuit of Multi-Trial Biosignals, with Application to Brain Signals

submittedTime-frequency representations are commonly used to analyze the oscillatory nature of bioelectromagnetic signals. There is a growing interest in sparse representations, where the data is described using few components. In this study, we adapt the Matching Pursuit of Mallat and Zhang for biosignals consisting of a series of variations around a similar pattern, with emphasis on multi-trial datasets encountered in MEG and EEG. The general principle of Matching Pursuit (MP) is to iteratively subtract from the signal its projection on the atom selected from a dictionary. The originality of our method is to select each atom using a voting technique that is robust to variability, and to subtract it by adapting the parameters to each trial. Because it is designed to handle inter-trial variability using a voting technique, the method is called Consensus Matching Pursuit (CMP). The method is validated on both simplified and realistic simulations, and on two real datasets (intracerebral EEG and scalp EEG ).We also compare our method to two other multi-trial MP algorithms: Multivariate MP (MMP) and Induced activity MP (IMP). CMP is shown to be able to sparsely reveal the structure present in the data, and to be robust to variability (jitter) across trials

Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study

International audienceElectrophysiological signals (electroencephalography, EEG, and magnetoencephalography , MEG), as many natural processes, exhibit scale-invariance properties resulting in a power-law (1/f) spectrum. Interestingly, EEG and MEG differ in their slopes, which could be explained by several mechanisms, including non-resistive properties of tissues. Our goal in the present study is to estimate the impact of space/frequency structure of source signals as a putative mechanism to explain spectral scaling properties of neuroimaging signals. We performed simulations based on the summed contribution of cortical patches with different sizes (ranging from 0.4 to 104.2 cm 2). Small patches were attributed signals of high frequencies, whereas large patches were associated with signals of low frequencies, on a logarithmic scale. The tested parameters included i) the space/frequency structure (range of patch sizes and frequencies) and ii) the amplitude factor c parametrizing the spatial scale ratios. We found that the space/frequency structure may cause differences between EEG and MEG scale-free spectra that are compatible with real data findings reported in previous studies. We also found that below a certain spatial scale, there were no more differences between EEG and MEG, suggesting a limit for the resolution of both methods. Our work provides an explanation of experimental findings. This does not rule out other mechanisms for differences between EEG and MEG, but suggests an important role of spatio-temporal structure of neural dynamics. This can help the analysis and interpretation of power-law measures in EEG and MEG, and we believe our results can also impact computational modeling of brain dynamics, where different local connectivity structures could be used at different frequencies

Sparse wavelet-based solutions for the M/EEG inverse problem

This paper is concerned with variational and Bayesian approaches to neuro-electromagnetic inverse problems (EEG and MEG). The strong indeterminacy of these problems is tackled by introducing sparsity inducing regularization/priors in a transformed domain, namely a spatial wavelet domain. Sparsity in the wavelet domain allows to reach ''data compression'' in the cortical sources domain. Spatial wavelets defined on the mesh graph of the triangulated cortical surface are used, in combination with sparse regression techniques, namely LASSO regression or sparse Bayesian learning, to provide localized and compressed estimates for brain activity from sensor data. Numerical results on simulated and real MEG data are provided, which outline the performances of the proposed approach in terms of localization

Independent component analysis reveals the unity of cognitive control

ISBN : 978-2-9532965-0-1In reaction time tasks, when subjects commit an error, a negative wave peaking approximatively 70-100ms after the erroneous response is recorded with EEG. This negativity, called "Error (Related) Negativity" (Ne or ERN[1, 2]), is maximal fronto-centrally, above the anterior cingulate cortex and/or SMA and was first interpreted as reflecting an error detection mechanism. However, after Laplacian estimation, a similar component was later observed on correct trials [3]. If this component on correct trials were to be the same as the one observed on errors, this would put important constraints on computational models of cognitive control. To address this issue we used Independent Com- ponent Analysis (ICA) to evaluate whether a single component (in ICA terms) could account for the waves observed in both erroneous and correct trials. For all the participants, a single component that accounts for the waves observed in the three categories of trials was found. The localisation of the sources is consistent with a rostral-cingulate zone origin, where control mechanisms are likely implemented [4]. This novel use of ICA allowed us to conclude that the negativities observed on error and correct trials are reflecting the same physiological mechanism whose amplitude is modulated as function of the performance

Time-frequency strategies for increasing high frequency oscillation detectability in intracerebral EEG

International audienceBackground: High Frequency Oscillations (HFOs) are considered to be highly representative of brain tissues capable of producing epileptic seizures. The visual review of HFOs on intracerebral electroencephalography is time-consuming and tedious, and it can be improved by time-frequency (TF) analysis. The main issue is that the signal is dominated by lower frequencies that mask the HFOs. Our aim was to flatten (i.e. whiten) the frequency spectrum to enhance the fast oscillations while preserving an optimal Signal to Noise Ratio (SNR). Method: We investigated 8 methods of data whitening based on either prewhitening or TF normalization in order to improve the detectability of HFOs. We detected all local maxima of the TF image above a range of thresholds in the HFO band. Results: We obtained the Precision and Recall curves at different SNR and for different HFO types and illustrate the added value of whitening both in the time-frequency plane and in time domain. Conclusion: The normalization strategies based on a baseline and on our proposed method (the " H0 z-score ") are more precise than the others. Significance: The H0 z-score provides an optimal framework for representing and detecting HFOs, independent of a baseline and a priori frequency bands

Studying connectivity between time-series using an interactive application

[Otros] Connectivity is a complex concept whose meaning varies for each domain. In a broad sense, it is possible to divide the connectivity in two main groups: physical and statistical. The former represents whether there is a physical link connecting the elements, like the telephone line, while the latter indicates a relation between the dynamics of the elements, as the number of sales and the income of a company. In several scientific fields, including economics, physics and neuroscience, the statistical connectivity is computed based on the analysis of time-series. Several different methods can be applied on the data, using different features of the signals, like covariance and frequency, to estimate the connectivity, yielding different results and interpretations. The covariance indicates if two elements follow the same dynamics, i.e., if they increase or decrease at the same time. On the other hand, the frequency is related to the time required for the elements to change, which can be daily in the case of the tides, or milliseconds if referred to the computations in a microchip. A third factor is the directionality, with one element depending on the other, but not in the opposite case. This work proposes an interactive computer-based application to generate a three-node network and estimate its connectivity following different approaches. We describe how the application is structured, from the selection of the parameters to the interpretation of the output results, detailing the skills in connectivity that can be developed by the students.López-Madrona, VJ.; Moratal, D.; Bénar, CG. (2021). Studying connectivity between time-series using an interactive application. IATED Academy. 6635-6638. https://doi.org/10.21125/edulearn.2021.1342S6635663

Propagation of epileptic spikes revealed by diffusion-based constrained MEG source reconstruction

International audienceGoal: Study of the propagation of an epileptic spike. Method: 1- cortex parcellation via structural information coming from diffusion MRI (dMRI) 2- MEG inverse problem on a parcellated source space 3- study of the propagation of an epileptic spike via the active parcels Results on real data allowing to study the spatial propagation of an epileptic spike

Application of a hemodynamic model to epileptic spikes

Despite the interest in simultaneous EEG-fMRI studies of epileptic spikes, the link between epileptic discharges and their corresponding hemodynamic responses is poorly understood. We applied two biophysical models in order to investigate the mechanisms underlying the neurovascular coupling in epilepsy: a metabolic hemodynamic model, and a neural mass model that simulates epileptic discharges. Analyzing the effect of epileptic neuronal activity on the BOLD response we focussed on the issues of linearity and on the origin of negative BOLD signals. In our BOLD simulation results both sub- and supra-linearity occur one after another. The size of these effects depends on the spike frequency, as well as on the amplitude of the excitatory part of the neural input. For the hemodynamic model used in this study, we found that the sign of the BOLD response is mainly determined by the area under the curve describing the excitatory neural activity. Therefore, a strong deactivation following the initial peak of the excitatory time course of an epileptic spike is necessary to obtain a negative BOLD