Literatura académica sobre el tema "Données EEG/MEG"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Données EEG/MEG".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Données EEG/MEG"
Mallet, L. "∑njeux de la πsychiatrie ℂomputationnelle". European Psychiatry 30, S2 (noviembre de 2015): S50—S51. http://dx.doi.org/10.1016/j.eurpsy.2015.09.143.
Texto completoCournoyer, A., V. Langlois-Carbonneau, R. Daoust y J. Chauny. "LO29: Création dune règle de décision clinique pour le diagnostic dun syndrome aortique aigu avec les outils dintelligence artificielle : phase initiale de définition des attributs communs aux patients sans syndrome aortique aigu chez une population à risque". CJEM 20, S1 (mayo de 2018): S16—S17. http://dx.doi.org/10.1017/cem.2018.91.
Texto completoMerlet, Isabelle. "Analyse dipolaire des paroxysmes intercritiques et critiques en EEG et MEG". Epileptic Disorders 3, SP1 (diciembre de 2001). http://dx.doi.org/10.1684/j.1950-6945.2001.tb00409.x.
Texto completoQuinn, Kieran L., Corita R. Grudzen, Alexander K. Smith y Allan S. Detsky. "Stop that Train! I Want to Get Off: Emergency Care for Patients with Advanced Dementia". Canadian Journal of General Internal Medicine 12, n.º 1 (9 de mayo de 2017). http://dx.doi.org/10.22374/cjgim.v12i1.205.
Texto completoTesis sobre el tema "Données EEG/MEG"
Belaoucha, Brahim. "Utilisation de l’IRM de diffusion pour la reconstruction de réseaux d’activations cérébrales à partir de données MEG/EEG". Thesis, Université Côte d'Azur (ComUE), 2017. http://www.theses.fr/2017AZUR4027/document.
Texto completoUnderstanding how brain regions interact to perform a given task is a very challenging task. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive functional imaging modalities used to record brain activity with high temporal resolution. As estimating brain activity from these measurements is an ill-posed problem. We thus must set a prior on the sources to obtain a unique solution. It has been shown in previous studies that structural homogeneity of brain regions reflect their functional homogeneity. One of the main goals of this work is to use this structural information to define priors to constrain more anatomically the MEG/EEG source reconstruction problem. This structural information is obtained using diffusion magnetic resonance imaging (dMRI), which is, as of today, the unique non-invasive structural imaging modality that provides an insight on the structural organization of white matter. This makes its use to constrain the EEG/MEG inverse problem justified. In our work, dMRI information is used to reconstruct brain activation in two ways: (1) In a spatial method which uses brain parcels to constrain the sources activity. These parcels are obtained by our whole brain parcellation algorithm which computes cortical regions with the most structural homogeneity with respect to a similarity measure. (2) In a spatio-temporal method that makes use of the anatomical connections computed from dMRI to constrain the sources’ dynamics. These different methods are validated using synthetic and real data
Ablin, Pierre. "Exploration of multivariate EEG /MEG signals using non-stationary models". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT051.
Texto completoIndependent Component Analysis (ICA) models a set of signals as linear combinations of independent sources. This analysis method plays a key role in electroencephalography (EEG) and magnetoencephalography (MEG) signal processing. Applied on such signals, it allows to isolate interesting brain sources, locate them, and separate them from artifacts. ICA belongs to the toolbox of many neuroscientists, and is a part of the processing pipeline of many research articles. Yet, the most widely used algorithms date back to the 90's. They are often quite slow, and stick to the standard ICA model, without more advanced features.The goal of this thesis is to develop practical ICA algorithms to help neuroscientists. We follow two axes. The first one is that of speed. We consider the optimization problems solved by two of the most widely used ICA algorithms by practitioners: Infomax and FastICA. We develop a novel technique based on preconditioning the L-BFGS algorithm with Hessian approximation. The resulting algorithm, Picard, is tailored for real data applications, where the independence assumption is never entirely true. On M/EEG data, it converges faster than the `historical' implementations.Another possibility to accelerate ICA is to use incremental methods, which process a few samples at a time instead of the whole dataset. Such methods have gained huge interest in the last years due to their ability to scale well to very large datasets. We propose an incremental algorithm for ICA, with important descent guarantees. As a consequence, the proposed algorithm is simple to use and does not have a critical and hard to tune parameter like a learning rate.In a second axis, we propose to incorporate noise in the ICA model. Such a model is notoriously hard to fit under the standard non-Gaussian hypothesis of ICA, and would render estimation extremely long. Instead, we rely on a spectral diversity assumption, which leads to a practical algorithm, SMICA. The noise model opens the door to new possibilities, like finer estimation of the sources, and use of ICA as a statistically sound dimension reduction technique. Thorough experiments on M/EEG datasets demonstrate the usefulness of this approach.All algorithms developed in this thesis are open-sourced and available online. The Picard algorithm is included in the largest M/EEG processing Python library, MNE and Matlab library, EEGlab
Jas, Mainak. "Contributions pour l'analyse automatique de signaux neuronaux". Electronic Thesis or Diss., Paris, ENST, 2018. http://www.theses.fr/2018ENST0021.
Texto completoElectrophysiology experiments has for long relied upon small cohorts of subjects to uncover statistically significant effects of interest. However, the low sample size translates into a low power which leads to a high false discovery rate, and hence a low rate of reproducibility. To address this issue means solving two related problems: first, how do we facilitate data sharing and reusability to build large datasets; and second, once big datasets are available, what tools can we build to analyze them ? In the first part of the thesis, we introduce a new data standard for sharing data known as the Brain Imaging Data Structure (BIDS), and its extension MEG-BIDS. Next, we introduce the reader to a typical electrophysiological pipeline analyzed with the MNE software package. We consider the different choices that users have to deal with at each stage of the pipeline and provide standard recommendations. Next, we focus our attention on tools to automate analysis of large datasets. We propose an automated tool to remove segments of data corrupted by artifacts. We develop an outlier detection algorithm based on tuning rejection thresholds. More importantly, we use the HCP data, which is manually annotated, to benchmark our algorithm against existing state-of-the-art methods. Finally, we use convolutional sparse coding to uncover structures in neural time series. We reformulate the existing approach in computer vision as a maximuma posteriori (MAP) inference problem to deal with heavy tailed distributions and high amplitude artifacts. Taken together, this thesis represents an attempt to shift from slow and manual methods of analysis to automated, reproducible analysis
Carrara, Igor. "Méthodes avancées de traitement des BCI-EEG pour améliorer la performance et la reproductibilité de la classification". Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4033.
Texto completoElectroencephalography (EEG) non-invasively measures the brain's electrical activity through electromagnetic fields generated by synchronized neuronal activity. This allows for the collection of multivariate time series data, capturing a trace of the brain electrical activity at the level of the scalp. At any given time instant, the measurements recorded by these sensors are linear combinations of the electrical activities from a set of underlying sources located in the cerebral cortex. These sources interact with one another according to a complex biophysical model, which remains poorly understood. In certain applications, such as surgical planning, it is crucial to accurately reconstruct these cortical electrical sources, a task known as solving the inverse problem of source reconstruction. While intellectually satisfying and potentially more precise, this approach requires the development and application of a subject-specific model, which is both expensive and technically demanding to achieve.However, it is often possible to directly use the EEG measurements at the level of the sensors and extract information about the brain activity. This significantly reduces the data analysis complexity compared to source-level approaches. These measurements can be used for a variety of applications, including monitoring cognitive states, diagnosing neurological conditions, and developing brain-computer interfaces (BCI). Actually, even though we do not have a complete understanding of brain signals, it is possible to generate direct communication between the brain and an external device using the BCI technology. This work is centered on EEG-based BCIs, which have several applications in various medical fields, like rehabilitation and communication for disabled individuals or in non-medical areas, including gaming and virtual reality.Despite its vast potential, BCI technology has not yet seen widespread use outside of laboratories. The primary objective of this PhD research is to try to address some of the current limitations of the BCI-EEG technology. Autoregressive models, even though they are not completely justified by biology, offer a versatile framework to effectively analyze EEG measurements. By leveraging these models, it is possible to create algorithms that combine nonlinear systems theory with the Riemannian-based approach to classify brain activity. The first contribution of this thesis is in this direction, with the creation of the Augmented Covariance Method (ACM). Building upon this foundation, the Block-Toeplitz Augmented Covariance Method (BT-ACM) represents a notable evolution, enhancing computational efficiency while maintaining its efficacy and versatility. Finally, the Phase-SPDNet work enables the integration of such methodologies into a Deep Learning approach that is particularly effective with a limited number of electrodes.Additionally, we proposed the creation of a pseudo online framework to better characterize the efficacy of BCI methods and the largest EEG-based BCI reproducibility study using the Mother of all BCI Benchmarks (MOABB) framework. This research seeks to promote greater reproducibility and trustworthiness in BCI studies.In conclusion, we address two critical challenges in the field of EEG-based brain-computer interfaces (BCIs): enhancing performance through advanced algorithmic development at the sensor level and improving reproducibility within the BCI community
Capítulos de libros sobre el tema "Données EEG/MEG"
van der Zwaard, Joke. "Meedoen versus zelf doen: Zin en onzin van stads-, buurt- en straatburgerschap". En Geleefd Burgerschap: Van eenheidsdwang naar ruimte voor verschil en vitaliteit, 154–68. Uitgeverij SWP, 2012. http://dx.doi.org/10.36254/978-90-8850-334-4.09.
Texto completo