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Objective. The aim of this paper is to present a novel method for simultaneous spike waveforms extraction and sorting from the raw recorded signal. The objective is twofold: on the one hand, to enhance spike sorting performance by extracting the spike waveforms of each spike and, on the other hand, to improve the analysis of the multi-scale relationships between spikes and local field potentials (LFP) by offering an accurate separation of these two components constitutive of the raw micro recordings. Approach. The method, based on a Bayesian approach, is fully automated and provides a mean spike shape for each cluster, but also an estimate for each singular spike waveform, as well as the LFP signal cleaned of spiking activity. Main results. The performance of the algorithm is evaluated on simulated and real data, for which both the clustering and spike removal aspects are analyzed. Clustering performance significantly increases when compared to state-of-the-art methods, taking benefit from the separation of the spikes from the LFP handled by our model. Our method also performs better in removing the spikes from the LFP when compared to previously proposed methodologies, especially in the high frequency bands. The method is finally applied on real data (ClinicalTrials.gov Identifier: NCT02877576) and confirm the results obtained on benchmark signals. Significance. By separating more efficiently the spikes from the LFP background, our method allows both a better spike sorting and a more accurate estimate of the LFP, facilitating further analysis such as spike-LFP relationships., (© 2023 IOP Publishing Ltd.)

A well known problem in EEG recordings deals with the unknown potential of the reference electrode. In the last years several authors presented comparisons among the most popular solutions, the global conclusion being that the traditional Average Reference (AR) and the Reference Standardization Technique (REST) are the best approximations (Nunez, 2010; Kayser and Tenke, 2010; Liu et al., 2015; Chella et al., 2016). In this work we do not aim to further compare these techniques but to support the fact that both solutions can be derived from a general inverse problem formalism for reference estimation (Hu et al., 2019; Hu et al., 2018; Salido-Ruiz et al., 2011). Using the alternative approach of least squares, our findings are consistent with the theoretical findings in Hu et al. (2019) and Hu et al. (2018) showing that the AR is the minimum norm solution, while REST is a weighted minimum norm including some approximate propagation model. AR is thus a particular case of REST, which itself uses a particular formulation of the source estimation inverse problem. With a different derivation, we provide the additional powerful evidences to reinforce the cited findings., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Objective: Cortical source imaging aims at identifying activated cortical areas on the surface of the cortex from the raw electroencephalogram (EEG) data. This problem is ill posed, the number of channels being very low compared to the number of possible source positions., Methods: In some realistic physiological situations, the active areas are sparse in space and of short time durations, and the amount of spatio-temporal data to carry the inversion is then limited. In this study, we propose an original data driven space-time-frequency (STF) dictionary which takes into account simultaneously both spatial and time-frequency sparseness while preserving smoothness in the time frequency (i.e., nonstationary smooth time courses in sparse locations). Based on these assumptions, we take benefit of the matching pursuit (MP) framework for selecting the most relevant atoms in this highly redundant dictionary., Results: We apply two recent MP algorithms, single best replacement (SBR) and source deflated matching pursuit, and we compare the results using a spatial dictionary and the proposed STF dictionary to demonstrate the improvements of our multidimensional approach. We also provide comparison using well-established inversion methods, FOCUSS and RAP-MUSIC, analyzing performances under different degrees of nonstationarity and signal to noise ratio., Conclusion: Our STF dictionary combined with the SBR approach provides robust performances on realistic simulations. From a computational point of view, the algorithm is embedded in the wavelet domain, ensuring high efficiency in term of computation time., Significance: The proposed approach ensures fast and accurate sparse cortical localizations on highly nonstationary and noisy data.

In difficult epileptic patients, the brain structures are explored by means of depth multicontact electrodes [stereoelectroencephalography (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 electrophysiological 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 singular spectrum analysis-GEVD approach.

In the context of drug resistant partial epilepsy, intra-cerebral electrical stimulation (Deep Brain Stimulation) constitutes one of the means of investigation to locate epileptic volume. This exogenous source can then activate the underlying epileptic networks and generate an electrophysiological reaction. The purpose of this work is to estimate and eliminate the overlapping electrical stimulation signal in order to subsequently explore the provoked underlying electrical activity. We propose here several methods to tackle this problem, using two different approaches based on different assumptions: BSS approach based on Independent Component Analysis (ICA) and non parametric decomposition - empirical modes decomposition (EMD) algorithms.

The starting point of this paper is the analysis of the reference problem in intra-cerebral electroencephalographic (iEEG) recordings. It is well accepted that both surface and depth EEG signals are always recorded with respect to some unknown time-varying signal called reference. This article discusses different methods for determining and reducing the influence of the reference signal for the iEEG signals. In particular, we derive optimal approaches for the estimation of the reference signal in iEEG recording setups and demonstrate their relation to the well-known minimum power/variance distortionless response approaches derived for general array and antenna signal processing applications. We show that the proposed approaches achieve optimal performance in terms of estimation error and that they outperform other reference identification methods proposed in the literature. The developed algorithms are illustrated on simulated examples and on real iEEG signals. [ABSTRACT FROM AUTHOR]