Magnétomètres à pompage optique à Hélium 4 : développement et preuve de concept en magnétocardiographie et en magnétoencéphalographie

Magnetocardiography (MCG) and magnetoencephalography (MEG) are non-invasive techniques consisting in measuring respectively cardiac and brain magnetic fields. Despite their performance, the sensors currently used need a cryogenic cooling system which engenders technical and financial constraints. New cryogenic-free sensors have recently emerged: the OPMs (Optically-Pumped Magnetometers). Among them, vector 4He magnetometers developed by CEA-LETI which work at room-temperature. This thesis is focused on the development of 4He magnetometers dedicated to MCG and MEG.After having optimized the key-parameters of a first non-miniaturized prototype, a sensitivity inferior to 100 fT/sqrt(Hz) has been obtained along two axes. In order to meet biomedical constraints, a miniaturization of the device has been processed. In parallel, preclinical tests were carried out which have enabled us to design a gradiometer mode, a new packaging, and a magnetically isolated system. A noise analysis revealed that laser and HF discharge were the main sources of disturbance, and lead us to consider improvements such as a new detection mode. Eventually, a sensor, with a 1cm-sided cell, and an intrinsic sensitivity of 350 fT/√Hz has been developed.Then, device has been successfully tested in the frame of the MCG application from a healthy subject, preceded by a simulation study with a phantom which enables us to demonstrate its operability within a clinical environment. Moreover, we have proved the reproducibility of the measurements and the possibility to detect the main features of the cardiac cycle within a 30 s recording time. A specific optimization of the optical part has enabled us to obtain a 210 fT/sqrt(Hz) sensitivity between 3 and 300 Hz, suitable for the MEG application. After having tested our device with a phantom, three MEG experiments were performed with a healthy subject: auditory evoked field, visual evoked field and spontaneous activities have been detected. The obtained results form the first clinical proof of concept of the device for MCG and MEG applications.La magnétocardiographie (MCG) et la magnétoencéphalographie (MEG) sont deux techniques d'imagerie non-invasives mesurant respectivement les champs magnétiques cardiaques et cérébraux. Les dispositifs actuels utilisent des capteurs supraconducteurs de haute performance mais nécessitant un dispositif de refroidissement cryogénique, engendrant de fortes contraintes tant techniques que financières. Les magnétomètres à pompage optique (OPM) tendent à constituer une réelle alternative. Parmi eux figurent ceux développés au CEA-LETI, basés sur l'utilisation de l'hélium 4. Cette thèse a pour objectif de développer des magnétomètres vectoriels à 4He (fonctionnant à température ambiante) dédiés aux applications MCG et MEG.Après une optimisation des paramètres-clés d'un prototype non-miniaturisé préexistant, une sensibilité inférieure à 100 fT/sqrt(Hz) a pu être obtenue suivant deux axes. Afin de respecter les besoins spécifiques de la MCG et de la MEG une étape de miniaturisation a dû être menée et une architecture gradient-métrique a été mise en place. Parallèlement, des tests précliniques menés à Clinatec nous ont permis de concevoir un nouveau conditionnement du prototype, ainsi qu'un système réduisant les perturbations magnétiques. Une analyse des principales sources de bruit a révélé que les deux principaux contributeurs au bruit sont le laser et le système de décharge HF. Nous avons ainsi envisagé plusieurs pistes d'amélioration du niveau de bruit dont une nouvelle technique de détection. Le prototype issu de ces travaux comporte une pièce élémentaire (la cellule) d'un centimètre de côté, et présente une sensibilité intrinsèque de 350 fT/sqrt(Hz).Le dispositif a ensuite été testé avec succès dans le cadre de mesures MCG sur un sujet sain, précédées de tests sur fantôme ayant permis de prouver l'opérabilité de nos capteurs dans un environnement clinique. Par ailleurs, la reproductibilité des résultats ainsi que la possibilité de réduire à 30 s le temps d'acquisition des données ont pu être démontrées. Une optimisation spécifique de la partie optique du prototype a permis d'obtenir une sensibilité de l'ordre de 210 fT/sqrt(Hz) entre 3 et 300 Hz, compatible avec l'application MEG. Après des tests menés avec succès sur fantôme, trois séries d'essais ont été réalisées sur un sujet sain. Nous avons pu ainsi détecter des potentiels évoqués auditifs, visuels ainsi qu'une modulation de l'activité cérébrale spontanée sous l'effet de l'ouverture des paupières. L'ensemble des résultats obtenus constitue les premières preuves de concept cliniques du dispositif en MCG et MEG

Electroencephalography and Magnetoencephalography

International audienceIn this chapter, we present the main characteristics of electroencephalography (EEG) and magnetoencephalography (MEG). More specifically, this chapter is dedicated to the presentation of the data, the way they can be acquired and analyzed. Then, we present the main features that can be extracted and their applications for brain disorders with concrete examples to illustrate them. Additional materials associated with this chapter are available in the dedicated Github repository

Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces

We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of non-invasive BCIs

Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user s mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability

Intentional binding enhances hybrid BCI control

Mental imagery-based brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system. Making BCIs efficient and accurate is paramount to improve the reliability of real-life and clinical applications, from open-loop device control to closed-loop neurorehabilitation. By promoting sense of agency and embodiment, realistic setups including multimodal channels of communication, such as eye-gaze, and robotic prostheses aim to improve BCI performance. However, how the mental imagery command should be integrated in those hybrid systems so as to ensure the best interaction is still poorly understood. To address this question, we performed a hybrid EEG-based BCI experiment involving healthy volunteers enrolled in a reach-and-grasp action operated by a robotic arm. Main results showed that the hand grasping motor imagery timing significantly affects the BCI accuracy as well as the spatiotemporal brain dynamics. Higher control accuracy was obtained when motor imagery is performed just after the robot reaching, as compared to before or during the movement. The proximity with the subsequent robot grasping favored intentional binding, led to stronger motor-related brain activity, and primed the ability of sensorimotor areas to integrate information from regions implicated in higher-order cognitive functions. Taken together, these findings provided fresh evidence about the effects of intentional binding on human behavior and cortical network dynamics that can be exploited to design a new generation of efficient brain-machine interfaces.Comment: 18 pages, 5 figures, 7 supplementary material

An OpenViBE Python-based framework for the efficient handling of MI BCI protocols

International audienceA typical Motor Imagery (MI) experimental pipeline is composed of an EEG data acquisition phase, followed by an analysis phase to train a classification algorithm (such as LDA). The training uses features extracted from the acquired data such as spectral power for a subset of sensors and frequencies, in which desynchronization can be observed between the MI tasks. In the final phase of the experiment, the trained classifier is used in an online setup.The feature selection phase is crucial for the optimal functioning of a BCI system, but the large number of parameters can make it a long step of trial-and-error, not acceptable in clinical settings. Training a classification algorithm can be challenging and time consuming as it may include multiple manipulations and datatype conversions with external softwares.Here, we propose a new Python-based framework to manage the whole experimental pipeline smoothly, integrating seamlessly with OpenViBE. We focused our work on feature analysis, selection, and classifier training. An easy-to-use GUI allows to keep track of the multiple acquired EEG signal files, and to process them for analysis and training. Convenient tools allow to compute spectral features and visualize them in the form of statistical R² maps, PSD, scalp topography and ERD/EDS time-frequency maps, for selected sensors or frequency bands, combining trials across multiple runs. Finally, a set of runs can be chosen for training the classification algorithm with only a few clicks and seconds of processing. All signal processing operations use OpenViBE in the background, transparent to the user.This framework has been successfully validated on real EEG data obtained with a Graz MI protocol. It allows the experimenter to identify the underlying brain processes during MI and choose the best combination of features for the subsequent classification. Work is ongoing to add further functionalities, notably functional connectivity

Ensemble learning based on functional connectivity and Riemannian geometry for robust workload estimation

International audienceContext Passive Brain-Computer Interface (pBCI) has recently gained in popularity through its applications, e.g. workload and attention assessment. Nevertheless, one of the main limitations remains the important intra-and inter-subject variability. We propose a robust approach relying on ensemble learning, grounded in functional connectivity and Riemannian geometry to mitigate the high variability of the data with a large and diverse panel of classifiers

Functional Connectivity for BCI: OpenViBE implementation

Présentations - Session 2International audienc