Alexandre Gramfort - Academia.edu (original) (raw)

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Papers by Alexandre Gramfort

NeuroImage, 2011

Although the spatial organization of visual areas can be revealed by functional Magnetic Resonanc... more Although the spatial organization of visual areas can be revealed by functional Magnetic Resonance Imaging (fMRI), the synoptic, non-invasive access to the temporal characteristics of the information flow amongst distributed visual processes remains a technical and methodological challenge. Using frequency-encoded steady-state visual stimulation together with a combination of time-resolved functional magnetic source imaging from magnetoencephalography (MEG) and anatomical magnetic resonance imaging (MRI), this study evidences maps of visuotopic sustained oscillatory neural responses distributed across the visual cortex. Our results further reveal relative phase delays across responding striate and extra-striate visual areas, which thereby shape the chronometry of neural processes amongst these regions. The methodology developed in this study points at further developments in time-resolved analyses of distributed visual processes in the millisecond range, and to new ways of exploring...

Frontiers in Neuroinformatics, 2011

We are interested by under-determined inverse problems, and more specifically by source localizat... more We are interested by under-determined inverse problems, and more specifically by source localization in magneto and electro- encephalography (M/EEG). In these problems, despite we have a physical model of diffusion (or "mixing") of the sources, the (very) under- determined aspect of the problem leads to a very diffucult inversion. The need of finding strong and physically relevant prior on the

2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, 2007

NeuroImage, 2015

Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electro... more Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain-computer interfaces (BCI) systems, in nearly all source localization methods for spatial whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for spatial whitening and source localization of MEG signals.

Lecture Notes in Computer Science, 2012

ABSTRACT Functional Magnetic Resonance Imaging (fMRI) provides an unique opportunity to study bra... more ABSTRACT Functional Magnetic Resonance Imaging (fMRI) provides an unique opportunity to study brain functional architecture, while being minimally invasive. Reverse inference, a.k.a. decoding, is a recent approach that has been used with success for deciphering activity patterns that are thought to fit the neuroscientific concept of population coding. This method can be used to assess the specificity of several brain regions for certain cognitive tasks, by evaluating the accuracy of the prediction of a behavioral variable of interest – the target – based on the activations measured in these regions. In this paper, we compare two important sub-problems that naturally arise: namely, which model is best suited for intra- versus inter-subject kind of decoding. While inter-subject prediction aims at finding predictive regions that are stable across subjects,it is plagued by the additional inter-subject variability (lack of voxel-to-voxel correspondence), so that the best suited prediction algorithms used in reverse inference may not be the same in both cases. We benchmark different prediction algorithms in both intra- and inter-subjects analysis, and we show that using spatial regularization improves reverse inference in the challenging context of inter-subject prediction.

2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013

2012 Second International Workshop on Pattern Recognition in NeuroImaging, 2012

2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009

2014 International Workshop on Pattern Recognition in Neuroimaging, 2014

Brain : a journal of neurology, 2014

In recent years, numerous electrophysiological signatures of consciousness have been proposed. He... more In recent years, numerous electrophysiological signatures of consciousness have been proposed. Here, we perform a systematic analysis of these electroencephalography markers by quantifying their efficiency in differentiating patients in a vegetative state from those in a minimally conscious or conscious state. Capitalizing on a review of previous experiments and current theories, we identify a series of measures that can be organized into four dimensions: (i) event-related potentials versus ongoing electroencephalography activity; (ii) local dynamics versus inter-electrode information exchange; (iii) spectral patterns versus information complexity; and (iv) average versus fluctuations over the recording session. We analysed a large set of 181 high-density electroencephalography recordings acquired in a 30 minutes protocol. We show that low-frequency power, electroencephalography complexity, and information exchange constitute the most reliable signatures of the conscious state. When...

NeuroImage, 2014

Multisensory interactions are ubiquitous in cortex and it has been suggested that sensory cortice... more Multisensory interactions are ubiquitous in cortex and it has been suggested that sensory cortices may be supramodal i.e. capable of functional selectivity irrespective of the sensory modality of inputs (Pascual-Leone and Hamilton, 2001; Renier et al., 2013; Ricciardi and Pietrini, 2011; Voss and Zatorre, 2012). Here, we asked whether learning to discriminate visual coherence could benefit from supramodal processing. To this end, three groups of participants were briefly trained to discriminate which of a red or green intermixed population of random-dot-kinematograms (RDKs) was most coherent in a visual display while being recorded with magnetoencephalography (MEG). During training, participants heard no sound (V), congruent acoustic textures (AV) or auditory noise (AVn); importantly, congruent acoustic textures shared the temporal statistics - i.e. coherence - of visual RDKs. After training, the AV group significantly outperformed participants trained in V and AVn although they wer...