Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities

BCI Motor Imagery datasets usually are small and have different electrodes setups. When training a Deep Neural Network, one may want to capitalize on all these datasets to increase the amount of data available and hence obtain good generalization results. To this end, we introduce a spatial graph signal interpolation technique, that allows to interpolate efficiently multiple electrodes. We conduct a set of experiments with five BCI Motor Imagery datasets comparing the proposed interpolation with spherical splines interpolation. We believe that this work provides novel ideas on how to leverage graphs to interpolate electrodes and on how to homogenize multiple datasets.Comment: Submitted to the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023

A Strong and Simple Deep Learning Baseline for BCI MI Decoding

We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a very simple baseline to compare to, using only very standard ingredients from the literature. We evaluate its performance on four EEG Motor Imagery datasets, including simulated online setups, and compare it to recent Deep Learning and Machine Learning approaches. EEG-SimpleConv is at least as good or far more efficient than other approaches, showing strong knowledge-transfer capabilities across subjects, at the cost of a low inference time. We advocate that using off-the-shelf ingredients rather than coming with ad-hoc solutions can significantly help the adoption of Deep Learning approaches for BCI. We make the code of the models and the experiments accessible

Une approche pour regrouper des sujets atteints de cancers, sur la base des similarités des répartitions cellulaires au sein de leurs biopsies

Full version in English: https://arxiv.org/abs/2007.00135International audienceExtended version in English: https://arxiv.org/abs/2007.00135In this paper, we introduce a novel and interpretable methodology to cluster subjects suffering from cancer, based on features extracted from their biopsies. Contrary to existing approaches, we propose here to capture complex patterns in the repartitions of their cells using histograms, and compare subjects on the basis of these repartitions. We describe here our complete workflow, including creation of the database, cells segmentation and phenotyping, computation of complex features, choice of a distance function between features, clustering between subjects using that distance, and survival analysis of obtained clusters. We illustrate our approach on a database of hematoxylin and eosin (H&E)-stained tissues of subjects suffering from Stage I lung adenocarcinoma, where our results match existing knowledge in prognosis estimation with high confidence.Nous présentons une méthodologie interprétable pour regrouper des sujets souffrant de cancer, basée sur des attributs extraits d'images numériques de leurs biopsies. Nous proposons d'analyser des répartitions cellulaires à l'aide d'histogrammes, et de comparer les sujets via ceux-ci. Nous décrivons ici la méthodologie pour définir ces histogrammes et les exploiter à des fins de clustering. Nous illustrons notre approche sur une base de données de tissus colorés à l'hématoxyline et à l'éosine de sujets atteints d'un adénocarcinome pulmonaire de stade I, où nos résultats coincident avec les connaissances existantes en matière d'estimation du pronostic de survie, avec une confiance statistique élevée

Pruning Graph Convolutional Networks to Select Meaningful Graph Frequencies for FMRI Decoding

International audienceGraph Signal Processing is a promising framework to manipulate brain signals as it allows to encompass the spatial dependencies between the activity in regions of interest in the brain. In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode fMRI signals. To this end, we introduce a deep learning architecture and adapt a pruning methodology to automatically identify such frequencies. We experiment with various datasets, architectures and graphs, and show that low graph frequencies are consistently identified as the most important for fMRI decoding, with a stronger contribution for the functional graph over the structural one. We believe that this work provides novel insights on how graph-based methods can be deployed to increase fMRI decoding accuracy and interpretability

Élagage de réseaux de neurones convolutifs sur graphes pour la sélection de fréquences significatives pour le décodage d'IRMf

International audienceGraph signal processing defines tools to manipulate signals evolving on irregular domains, such as brain signals, by encompassing the spatial dependencies between regions of interest in the brain. In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode Functional Magnetic Resonance Imaging (fMRI) signals. For that, we introduce a deep learning architecture and adapt a pruning methodology to automatically identify such frequencies. Our experiments show that low graph frequencies are consistently identified as the most important for fMRI decoding, with a stronger contribution for the functional graph over the structural one.Le traitement du signal sur graphe permet de manipuler des signaux évoluant sur des structures irrégulières, comme par exemple les signaux cérébraux, en exploitant les dépendances spatiales entre les régions d'intérêt dans le cerveau. Dans ce contexte, nous nous intéressons à mieux comprendre quelles sont les fréquences du graphe les plus utiles pour décoder des signaux d'Imagerie par Résonance Magnétique Fonctionnelle (IRMf). À cette fin, nous introduisons une architecture d'apprentissage profond et adaptons une méthode d'élagage pour identifier automatiquement ces fréquences. Nos expériences montrent que les basses fréquences des graphes sont systématiquement identifiées comme les plus importantes, avec une contribution plus forte pour le graphe fonctionnel que pour le graphe structurel