Rolando Grave de Peralta Menendez

University of Geneva, Genève, GE, Switzerland

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Publications (41)127.3 Total impact

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    Clinical EEG and neuroscience: official journal of the EEG and Clinical Neuroscience Society (ENCS) 12/2013; · 1.82 Impact Factor
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    Clinical EEG and neuroscience: official journal of the EEG and Clinical Neuroscience Society (ENCS) 12/2013; 44(4):E1-121. · 1.82 Impact Factor
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    S L Gonzalez Andino, R Grave de Peralta Menendez
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    ABSTRACT: Salient parts of a visual scene attract longer and earlier fixations of the eyes. Saliency is driven by bottom-up (image dependent) factors and top-down factors such as behavioral relevance, goals, and expertise. It is currently assumed that a saliency map defining eye fixation priorities is stored in neural structures that remain to be determined. Lesion studies support a role for the amygdala in detecting saliency. Here we show that neurons in the amygdala of primates fire differentially when the eyes approach to or fixate behaviorally relevant parts of visual scenes. Ensemble bursting in the amygdala accurately predicts main fixations during the free-viewing of natural images. However, fixation prediction is significantly better for faces-where a bottom-up computational saliency model fails-compared to unfamiliar objects and landscapes. On this basis we propose the amygdala as a locus for a saliency map and ensemble bursting as a saliency coding mechanism.
    Frontiers in Behavioral Neuroscience 01/2012; 6:38. · 4.76 Impact Factor
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    Sara L Gonzalez Andino, Stephen Perrig, Rolando Grave de Peralta Menendez
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    ABSTRACT: Recordings of brain electrophysiological activity provide the most direct reflect of neural function. Information contained in these signals varies as a function of the spatial scale at which recordings are done: from single cell recording to large scale macroscopic fields, e.g., scalp EEG. Microscopic and macroscopic measurements and models in Neuroscience are often in conflict. Solving this conflict might require the developments of a sort of bio-statistical physics, a framework for relating the microscopic properties of individual cells to the macroscopic or bulk properties of neural circuits. Such a framework can only emerge in Neuroscience from the systematic analysis and modeling of the diverse recording scales from simultaneous measurements. In this article we briefly review the different measurement scales and models in modern neuroscience to try to identify the sources of conflict that might ultimately help to create a unified theory of brain electromagnetic fields. We argue that seen the different recording scales, from the single cell to the large scale fields measured by the scalp electroencephalogram, as derived from a unique physical magnitude--the electric potential that is measured in all cases--might help to conciliate microscopic and macroscopic models of neural function as well as the animal and human neuroscience literature.
    Medical & Biological Engineering 05/2011; 49(5):511-20. · 1.76 Impact Factor
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    Rolando Grave de Peralta Menendez, Olaf Hauk, Sara González Andino
    Comp. Int. and Neurosc. 01/2009; 2009.
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    Sara L. Gonzalez Andino, Rolando Grave de Peralta Menendez, Asaid Khateb, Theodor Landis, Alan J. Pegna
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    ABSTRACT: An EEG investigation was carried out in a patient with complete cortical blindness who presented affective blindsight, i.e. who performed above chance when asked to guess the emotional expressions on a series of faces. To uncover the electrophysiological mechanisms involved in this phenomenon we combined multivariate pattern recognition (MPR) with local field potential estimates provided by electric source imaging (ELECTRA). All faces, including neutral faces, elicited distinctive oscillatory EEG patterns that were correctly identified by the MPR algorithm as belonging to the class of facial expressions actually presented. Consequently, neural responses in this patient are not restricted to emotionally laden faces. Earliest non-specific differences between faces occur from 70 ms onwards in the superior temporal polysensory area (STP). Emotion-specific responses were found after 120 ms in the right anterior areas with right amygdala activation observed only later (∼ 200 ms). Thus, affective blindsight might be mediated by subcortical afferents to temporal areas as suggested in some studies involving non-emotional stimuli. The early activation of the STP in the patient constitutes evidence for fast activation of higher order visual areas in humans despite bilateral V1 destruction. In addition, the absence of awareness of any visual experience in this patient suggests that neither the extrastriate visual areas, nor the prefrontal cortex activation alone are sufficient for conscious perception, which might require recurrent processing within a network of several cerebral areas including V1.
    NeuroImage 01/2009; · 6.25 Impact Factor
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    ABSTRACT: We present the four key areas of research — preprocessing, the volume conductor, the forward problem, and the inverse problem — that affect the performance of EEG and MEG source imaging. In each key area we identify prominent approaches and methodologies that have open issues warranting further investigation within the community, challenges associated with certain techniques, and algorithms necessitating clarification of their implications. More than providing definitive answers we aim to identify important open issues in the quest of source localization.
    Computational Intelligence and Neuroscience 01/2009; 2009.
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    ABSTRACT: Recent public demonstrations showed that a system based on imagination does not always work (1). On the other side predicting limb movement based on scalp activity has proved to be hazardous (2) and thus other alternatives are needed. This paper describes the asynchronous Geneva-BCI based on EEG and visual attention to external stimulus able to send commands every 0.5 (or 0.25) seconds with very high (98.88%) correct classification rates and optimal (178 bits/min) theoretical bit rate. This high performance allows for the distant real time control of robots using four commands.
    ESANN 2009, 17th European Symposium on Artificial Neural Networks, Bruges, Belgium, April 22-24, 2009, Proceedings; 01/2009
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    ABSTRACT: The relationship between electrophysiological and functional magnetic resonance imaging (fMRI) signals remains poorly understood. To date, studies have required invasive methods and have been limited to single functional regions and thus cannot account for possible variations across brain regions. Here we present a method that uses fMRI data and singe-trial electroencephalography (EEG) analyses to assess the spatial and spectral dependencies between the blood-oxygenation-level-dependent (BOLD) responses and the noninvasively estimated local field potentials (eLFPs) over a wide range of frequencies (0-256 Hz) throughout the entire brain volume. This method was applied in a study where human subjects completed separate fMRI and EEG sessions while performing a passive visual task. Intracranial LFPs were estimated from the scalp-recorded data using the ELECTRA source model. We compared statistical images from BOLD signals with statistical images of each frequency of the eLFPs. In agreement with previous studies in animals, we found a significant correspondence between LFP and BOLD statistical images in the gamma band (44-78 Hz) within primary visual cortices. In addition, significant correspondence was observed at low frequencies (<14 Hz) and also at very high frequencies (>100 Hz). Effects within extrastriate visual areas showed a different correspondence that not only included those frequency ranges observed in primary cortices but also additional frequencies. Results therefore suggest that the relationship between electrophysiological and hemodynamic signals thus might vary both as a function of frequency and anatomical region.
    Journal of Neurophysiology 11/2008; 101(1):491-502. · 3.30 Impact Factor
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    ABSTRACT: Surprisingly effortless is the human capacity known as "mentalizing", i.e., the ability to explain and predict the behavior of others by attributing to them independent mental states, such as beliefs, desires, emotions or intentions. This capacity is, among other factors, dependent on the correct anticipation of the dynamics of facially expressed emotions based on our beliefs and experience. Important information about the neural processes involved in mentalizing can be derived from dynamic recordings of neural activity such as the EEG. We here exemplify how the so-called Bayesian probabilistic models can help us to model the neural dynamic involved in the perception of clips that evolve from neutral to emotionally laden faces. Contrasting with conventional models, in Bayesian models, probabilities can be used to dynamically update beliefs based on new incoming information. Our results show that a reproducible model of the neural dynamic involved in the appraisal of facial expression can be derived from the grand mean ERP over five subjects. One of the two models used to predict the individual subject dynamic yield correct estimates for four of the five subjects analyzed. These results encourage the future use of Bayesian formalism to build more detailed models able to describe the single trial dynamic.
    Brain Topography 07/2008; 20(4):278-83. · 3.67 Impact Factor
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    Rolando Grave de Peralta Menendez, Sara L. Gonzalez Andino
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    ABSTRACT: This chapter shows that there is a mathematical relationship between potentials measured at the scalp (EEG) and a scalar field inside the brain. This scalar field is a potential field for the current source density vector sharing the same sources and sinks of the intracranial potential measured inside the brain volume. The estimation of this potential field is mathematically equivalent to the use of the irrotational source model of ELECTRA inverse solution and for that reason it is denoted as eLFP. Extensive theoretical and practical elements needed to understand and implement the estimation of eLFP are also included. The simulations presented shed some light on the basic questions that can arise in front of these estimates, i.e., how much information can be obtained from the eLFP in comparison with the EEG used for their to estimation and in comparison with invasive intracranial recordings. As described in Table I, the range of CC (in %) values observed for the eLFP estimated from the EEG in healthy subjects (91-98) are not lower than the CC values (91 and 94) obtained from invasive recordings in two patients. This could be because intracranial electrodes are not located to optimize classification but to study the neurological conditions of the patients. However, it could be also an evidence for the use of the non-invasive method proposed to guide the positioning of intracranial electrodes. Further investigation will be needed to confirm that.
    01/2008; , ISBN: 978-3-902613-18-9
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    ABSTRACT: Modern electrophysiological studies in animals show that the spectrum of neural oscillations encoding relevant information is broader than previously thought and that many diverse areas are engaged for very simple tasks. However, EEG-based brain-computer interfaces (BCI) still employ as control modality relatively slow brain rhythms or features derived from preselected frequencies and scalp locations. Here, we describe the strategy and the algorithms we have developed for the analysis of electrophysiological data and demonstrate their capacity to lead to faster accurate decisions based on linear classifiers. To illustrate this strategy, we analyzed two typical BCI tasks. (1) Mu-rhythm control of a cursor movement by a paraplegic patient. For this data, we show that although the patient received extensive training in mu-rhythm control, valuable information about movement imagination is present on the untrained high-frequency rhythms. This is the first demonstration of the importance of high-frequency rhythms in imagined limb movements. (2) Self-paced finger tapping task in three healthy subjects including the data set used in the BCI-2003 competition. We show that by selecting electrodes and frequency ranges based on their discriminative power, the classification rates can be systematically improved with respect to results published thus far.
    Computational Intelligence and Neuroscience 02/2007;
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    Sara L Gonzalez Andino, Micah M Murray, John J Foxe, Rolando Grave de Peralta Menendez
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    ABSTRACT: This study details a method to statistically determine, on a millisecond scale and for individual subjects, those brain areas whose activity differs between experimental conditions, using single-trial scalp-recorded EEG data. To do this, we non-invasively estimated local field potentials (LFPs) using the ELECTRA distributed inverse solution and applied non-parametric statistical tests at each brain voxel and for each time point. This yields a spatio-temporal activation pattern of differential brain responses. The method is illustrated here in the analysis of auditory-somatosensory (AS) multisensory interactions in four subjects. Differential multisensory responses were temporally and spatially consistent across individuals, with onset at approximately 50 ms and superposition within areas of the posterior superior temporal cortex that have traditionally been considered auditory in their function. The close agreement of these results with previous investigations of AS multisensory interactions suggests that the present approach constitutes a reliable method for studying multisensory processing with the temporal and spatial resolution required to elucidate several existing questions in this field. In particular, the present analyses permit a more direct comparison between human and animal studies of multisensory interactions and can be extended to examine correlation between electrophysiological phenomena and behavior.
    Experimental Brain Research 11/2005; 166(3-4):298-304. · 2.22 Impact Factor
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    NeuroImage 01/2005; 25(2):652-652. · 6.25 Impact Factor
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    ABSTRACT: Recent experiments have shown the possibility to use the brain electrical activity to directly control the movement of robots or prosthetic devices in real time. Such neuroprostheses can be invasive or non-invasive, depending on how the brain signals are recorded. In principle, invasive approaches will provide a more natural and flexible control of neuroprostheses, but their use in humans is debatable given the inherent medical risks. Non-invasive approaches mainly use scalp electroencephalogram (EEG) signals and their main disadvantage is that these signals represent the noisy spatiotemporal overlapping of activity arising from very diverse brain regions; i.e., a single scalp electrode picks up and mixes the temporal activity of myriads of neurons at very different brain areas. In order to combine the benefits of both approaches, we propose to rely on the non-invasive estimation of local field potentials (LFP) in the whole human brain from the scalp measured EEG data using a recently developed inverse solution (ELECTRA) to the EEG inverse problem. The goal of a linear inverse procedure is to de-convolve or un-mix the scalp signals attributing to each brain area its own temporal activity. To illustrate the advantage of this approach we compare, using identical set of spectral features, classification of rapid voluntary finger self-tapping with left and right hands based on scalp EEG and non-invasively estimated LFP on two subjects using different number of electrodes.
    Cognitive Processing 01/2005; 6:59-64. · 1.57 Impact Factor
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    R. Grave de Peralta Menendez, M.M. Murray, S.L.G. Andino
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    ABSTRACT: This paper proposes a new strategy for improving the localization capabilities of linear inverse solutions, based on the relationship between the real solution and the estimated solution as described by the resolution matrix equation. Specifically, we present two alternatives based on either the partial or total inversion of the resolution matrix and applied them to the minimum norm solution, which is known for its poor performance in three-dimensional (3-D) localization problems. The minimum norm transformed inverse showed a clear improvement in 3-D localization. The strong dependence of localization errors with the eccentricity of the sources, characteristic of this solution, disappears after the proposed transformation. A similar effect is illustrated, using a realistic example where multiple generators at striate areas are active. While the original minimum norm incorrectly places the generators at extrastriate cortex, the transformed minimum norm localizes, for the example considered, the sources at their correct eccentricity with very low spatial bluffing.
    IEEE Transactions on Biomedical Engineering 10/2004; · 2.35 Impact Factor
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    ABSTRACT: This paper proposes and implements biophysical constraints to select a unique solution to the bioelectromagnetic inverse problem. It first shows that the brain's electric fields and potentials are predominantly due to ohmic currents. This serves to reformulate the inverse problem in terms of a restricted source model permitting noninvasive estimations of Local Field Potentials (LFPs) in depth from scalp-recorded data. Uniqueness in the solution is achieved by a physically derived regularization strategy that imposes a spatial structure on the solution based upon the physical laws that describe electromagnetic fields in biological media. The regularization strategy and the source model emulate the properties of brain activity's actual generators. This added information is independent of both the recorded data and head model and suffices for obtaining a unique solution compatible with and aimed at analyzing experimental data. The inverse solution's features are evaluated with event-related potentials (ERPs) from a healthy subject performing a visuo-motor task. Two aspects are addressed: the concordance between available neurophysiological evidence and inverse solution results, and the functional localization provided by fMRI data from the same subject under identical experimental conditions. The localization results are spatially and temporally concordant with experimental evidence, and the areas detected as functionally activated in both imaging modalities are similar, providing indices of localization accuracy. We conclude that biophysically driven inverse solutions offer a novel and reliable possibility for studying brain function with the temporal resolution required to advance our understanding of the brain's functional networks.
    NeuroImage 03/2004; 21(2):527-39. · 6.25 Impact Factor
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    ABSTRACT: The relationship between interictal epileptiform activity and the epileptogenic zone is complex. Despite the fact that intraspike propagation may occur, the peak of the spike is often used as indicator of the site of ictal onset. In this investigation, spatio-temporal segmentation was used to demonstrate this intraspike propagation and to determine at which time point the voltage pattern corresponded best to the epileptogenic zone. Sixteen patients with focal epilepsy were recorded with 125-channel EEG. Between one and five different map topographies were identified during the rising phase of the spike. A distributed source model (EPIFOCUS) was used to localize the source of each map, and the distance from the EPIFOCUS maximum to the anatomic lesion was calculated. In only 3 of 16 cases was the entire rising phase of the spike accounted for by one single map. In another five patients, several maps were obtained, although all were located within the epileptogenic lesion. In the remaining eight patients, however, parts of the rising phase had locations outside the epileptogenic lesion. On the average, 80% of the rising time had within lesion locations the most reliable time period being halfway between onset and peak. The results illustrate that intraspike propagation has to be considered in source localizations, and they also illustrate the usefulness of spatio-temporal segmentation for visualizing this propagation.
    Journal of Clinical Neurophysiology 01/2003; 20(5):311-9. · 1.45 Impact Factor
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    S L Gonzalez Andino, R Grave de Peralta Menendez, C M Lantz, O Blank, C M Michel, T Landis
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    ABSTRACT: Localization of the generators of the scalp measured electrical activity is particularly difficult when a large number of brain regions are simultaneously active. In this study, we describe an approach to automatically isolate scalp potential maps, which are simple enough to expect reasonable results after applying a distributed source localization procedure. The isolation technique is based on the time-frequency decomposition of the scalp-measured data by means of a time-frequency representation. The basic rationale behind the approach is that neural generators synchronize during short time periods over given frequency bands for the codification of information and its transmission. Consequently potential patterns specific for certain time-frequency pairs should be simpler than those appearing at single times but for all frequencies. The method generalizes the FFT approximation to the case of distributed source models with non-stationary time behavior. In summary, the non-stationary distributed source approximation aims to facilitate the localization of distributed source patterns acting at specific time and frequencies for non-stationary data such as epileptic seizures and single trial event related potentials. The merits of this approach are illustrated here in the analysis of synthetic data as well as in the localization of the epileptogenic area at seizure onset in patients. It is shown that time and frequency at seizure onset can be precisely detected in the time-frequency domain and those localization results are stable over seizures. The results suggest that the method could also be applied to localize generators in single trial evoked responses or spontaneous activity.
    Human Brain Mapping 11/2001; 14(2):81-95. · 6.88 Impact Factor
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    ABSTRACT: Functional mapping of the human brain has made tremendous progress in the past years thanks to new technical developments. Imaging methods are now available; they allow to study brain functions with high spatial and temporal resolution. Single photon emission computer tomography (SPECT), positron emission tomography (PET), functional magnetic resonance imaging (fMRI) and high resolution electro- and magnetoencephalography (EEG and MEG) are currently intensively applied techniques to functional studies, each one having specific properties concerning spatial and temporal resolution. The success of these methods in basic neuroscience research has led to the demand for applying them to clinical questions. Diseases of the central nervous system that lead to brain dysfunction can be ideally explored using these techniques. Of particular importance are those diseases in which a focal neuronal dysfunction is the primary cause and where surgical resection of this focus might be the cure. This is often the case for epilepsy, where a discrete primary focus might exist from which pathological rhythms evolve and propagate throughout the brain, leading to seizures that severely handicap the patient. Surgical resection of the primary focus is only possible if the focus can be exactly localized and adequately separated from functionally important areas. This is where these new functional imaging tools become important. The use of SPECT and PET for focus localization has been most extensively studied and their specificity and sensitivity are intensively discussed. In the last few years functional MRI has evolved as a new interesting tool in epileptic focus localization. The most important limitation of these techniques, however, is the temporal resolution. Since epileptic activity can propagate very fast, several hyper- or hypoactive regions are seen in the images and primary areas cannot be distinguished from regions of propagation. The only methods that have sufficient temporal resolution to follow neuronal activity in real time are the electrophysiological measures, i.e. the EEG and the MEG. Localization of the sources in the brain that produced a given surface electromagnetic field has become possible through algorithms that solve the so-called "inverse problem". Several different algorithms exist and many groups begun to apply them to epileptic data with the aim to localize the focus of the pathological electrical discharges. This review article discusses the use of distributed EEG source localization procedures in the presurgical evaluation of patients with intractable focal epilepsy. In contrast to equivalent dipole models, distributed localization methods do not localize one active point in the brain but rather assume extended active areas, which is generally the case in epileptic activity. The methods shown here are based on linear numerical methods and are therefore less prone to errors when working with scattered solution spaces such as the one defined by anatomical constraints. Solutions constraint to the gray matter determined in the individual MRI are shown here. We illustrate three methods to increase the spatial resolution of the source localization procedures: One is to increase the number of recording channels to more than 100, the second to use linear methods of high precision to detect focal sources (EPIFOCUS), and the third to combine EEG source localization with EEG-triggered functional magnetic resonance imaging. The importance of EEG source localization for the interpretation of fMRI data will be particularly discussed in view of the important difference of the temporal resolution by the two methods. The localization methods can be applied to interictal as well as to ictal activity. In case of analysis of ictal EEG we propose to use full scalp frequency analysis to determine the time period of seizure onset and to localize the sources of the initial dominant frequency.
    Epileptic disorders: international epilepsy journal with videotape 08/2001; Special Issue:45-58. · 1.17 Impact Factor

Publication Stats

852 Citations
127.30 Total Impact Points

Institutions

  • 1997–2012
    • University of Geneva
      • • Department of Clinical Neuroscience
      • • Division of Neurology
      Genève, GE, Switzerland
    • IFZ Graz
      Gratz, Styria, Austria
  • 2009
    • University of Barcelona
      Barcino, Catalonia, Spain
    • Tampere University of Technology
      Tammerfors, Province of Western Finland, Finland
  • 2008
    • University Hospital of Lausanne
      Lausanne, Vaud, Switzerland
  • 1994
    • Cuban Neurosciences Center
      La Habana, Ciudad de La Habana, Cuba