Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems

International audienc

The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System

Combining low cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. We present a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system - Smartphone Brain Scanner - combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully mobile system for real-time 3D EEG imaging. We discuss the benefits and challenges of a fully portable system, including technical limitations as well as real-time reconstruction of 3D images of brain activity. We present examples of the brain activity captured in a simple experiment involving imagined finger tapping, showing that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using a off-the-shelf consumer neuroheadset is lower compared to that obtained using high density standard EEG equipment, we propose that mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings

An embedding for EEG signals learned using a triplet loss

Neurophysiological time series recordings like the electroencephalogram (EEG) or local field potentials are obtained from multiple sensors. They can be decoded by machine learning models in order to estimate the ongoing brain state of a patient or healthy user. In a brain-computer interface (BCI), this decoded brain state information can be used with minimal time delay to either control an application, e.g., for communication or for rehabilitation after stroke, or to passively monitor the ongoing brain state of the subject, e.g., in a demanding work environment. A specific challenge in such decoding tasks is posed by the small dataset sizes in BCI compared to other domains of machine learning like computer vision or natural language processing. A possibility to tackle classification or regression problems in BCI despite small training data sets is through transfer learning, which utilizes data from other sessions, subjects or even datasets to train a model. In this exploratory study, we propose novel domain-specific embeddings for neurophysiological data. Our approach is based on metric learning and builds upon the recently proposed ladder loss. Using embeddings allowed us to benefit, both from the good generalisation abilities and robustness of deep learning and from the fast training of classical machine learning models for subject-specific calibration. In offline analyses using EEG data of 14 subjects, we tested the embeddings' feasibility and compared their efficiency with state-of-the-art deep learning models and conventional machine learning pipelines. In summary, we propose the use of metric learning to obtain pre-trained embeddings of EEG-BCI data as a means to incorporate domain knowledge and to reach competitive performance on novel subjects with minimal calibration requirements.Comment: 23 pages, 11 figures, 5 appendix pages, 6 appendix figures, work conducted in 2020-2021 during an ARPE (https://ens-paris-saclay.fr/en/masters/ens-paris-saclay-degree/year-pre-doctoral-research-abroad-arpe

Translational pipelines for closed-loop neuromodulation

Closed-loop neuromodulation systems have shown significant potential for addressing unmet needs in the treatment of disorders of the central nervous system, yet progress towards clinical adoption has been slow. Advanced technological developments often stall in the preclinical stage by failing to account for the constraints of implantable medical devices, and due to the lack of research platforms with a translational focus. This thesis presents the development of three clinically relevant research systems focusing on refinements of deep brain stimulation therapies. First, we introduce a system for synchronising implanted and external stimulation devices, allowing for research into multi-site stimulation paradigms, cross-region neural plasticity, and questions of phase coupling. The proposed design aims to sidestep the limited communication capabilities of existing commercial implant systems in providing a stimulation state readout without reliance on telemetry, creating a cross-platform research tool. Next, we present work on the Picostim-DyNeuMo adaptive neuromodulation platform, focusing on expanding device capabilities from activity and circadian adaptation to bioelectric marker--based responsive stimulation. Here, we introduce a computationally optimised implementation of a popular band power--estimation algorithm suitable for deployment in the DyNeuMo system. The new algorithmic capability was externally validated to establish neural state classification performance in two widely-researched use cases: Parkinsonian beta bursts and seizures. For in vivo validation, a pilot experiment is presented demonstrating responsive neurostimulation to cortical alpha-band activity in a non-human primate model for the modulation of attention state. Finally, we turn our focus to the validation of a recently developed method to provide computationally efficient real-time phase estimation. Following theoretical analysis, the method is integrated into the commonly used Intan electrophysiological recording platform, creating a novel closed-loop optogenetics research platform. The performance of the research system is characterised through a pilot experiment, targeting the modulation of cortical theta-band activity in a transgenic mouse model

Predictive decoding of neural data

In the last five decades the number of techniques available for non-invasive functional imaging has increased dramatically. Researchers today can choose from a variety of imaging modalities that include EEG, MEG, PET, SPECT, MRI, and fMRI. This doctoral dissertation offers a methodology for the reliable analysis of neural data at different levels of investigation. By using statistical learning algorithms the proposed approach allows single-trial analysis of various neural data by decoding them into variables of interest. Unbiased testing of the decoder on new samples of the data provides a generalization assessment of decoding performance reliability. Through consecutive analysis of the constructed decoder\u27s sensitivity it is possible to identify neural signal components relevant to the task of interest. The proposed methodology accounts for covariance and causality structures present in the signal. This feature makes it more powerful than conventional univariate methods which currently dominate the neuroscience field. Chapter 2 describes the generic approach toward the analysis of neural data using statistical learning algorithms. Chapter 3 presents an analysis of results from four neural data modalities: extracellular recordings, EEG, MEG, and fMRI. These examples demonstrate the ability of the approach to reveal neural data components which cannot be uncovered with conventional methods. A further extension of the methodology, Chapter 4 is used to analyze data from multiple neural data modalities: EEG and fMRI. The reliable mapping of data from one modality into the other provides a better understanding of the underlying neural processes. By allowing the spatial-temporal exploration of neural signals under loose modeling assumptions, it removes potential bias in the analysis of neural data due to otherwise possible forward model misspecification. The proposed methodology has been formalized into a free and open source Python framework for statistical learning based data analysis. This framework, PyMVPA, is described in Chapter 5

Artificial intelligence techniques for studying neural functions in coma and sleep disorders

The use of artificial intelligence in computational neuroscience has increased within the last years. In the field of electroencephalography (EEG) research machine and deep learning, models show huge potential. EEG data is high dimensional, and complex models are well suited for their analysis. However, the use of artificial intelligence in EEG research and clinical applications is not yet established, and multiple challenges remain to be addressed. This thesis is focused on analyzing neurological EEG signals for clinical applications with artificial intelligence and is split into three sub-projects. The first project is a methodological contribution, presenting a proof of concept that deep learning on EEG signals can be used as a multivariate pattern analysis technique for research. Even though the field of deep learning for EEG has produced many publications, the use of these algorithms in research for the analysis of EEG signals is not established. Therefore for my first project, I developed an analysis pipeline based on a deep learning architecture, data augmentation techniques, and feature extraction method that is class and trial-specific. In summary, I present a novel multivariate pattern analysis pipeline for EEG data based on deep learning that can extract in a data-driven way trial-by-trial discriminant activity. In the second part of this thesis, I present a clinical application of predicting the outcome of comatose patients after cardiac arrest. Outcome prediction of patients in a coma is today still an open challenge, that depends on subjective clinical evaluations. Importantly, current clinical markers can leave up to a third of patients without a clear prognosis. To address this challenge, I trained a convolutional neural network on EEG signals of coma patients that were exposed to standardized auditory stimulations. This work showed a high predictive power of the trained deep learning model, also on patients that were without a established prognosis based on existing clinical criteria. These results emphasize the potential of deep learning models for predicting outcome of coma and assisting clinicians. In the last part of my thesis, I focused on sleep-wake disorders and studied whether unsupervised machine learning techniques could improve diagnosis. The field of sleep-wake disorders is convoluted, as they can cooccur within patients, and only a few disorders have clear diagnostic biomarkers. Thus I developed a pipeline based on an unsupervised clustering algorithm to disentangle the full landscape of sleep-wake disorders. First I reproduced previous results in a sub-cohort of patients with central disorders of hypersomnolence. The verified pipeline was then used on the full landscape of sleep-wake disorders, where I identified clear clusters of disorders with clear diagnostic biomarkers. My results call for new biomarkers, to improve patient phenotyping

Understanding and Decoding Imagined Speech using Electrocorticographic Recordings in Humans

Certain brain disorders, resulting from brainstem infarcts, traumatic brain injury, stroke and amyotrophic lateral sclerosis, limit verbal communication despite the patient being fully aware. People that cannot communicate due to neurological disorders would benefit from a system that can infer internal speech directly from brain signals. Investigating how the human cortex encodes imagined speech remains a difficult challenge, due to the lack of behavioral and observable measures. As a consequence, the fine temporal properties of speech cannot be synchronized precisely with brain signals during internal subjective experiences, like imagined speech. This thesis aims at understanding and decoding the neural correlates of imagined speech (also called internal speech or covert speech), for targeting speech neuroprostheses. In this exploratory work, various imagined speech features, such as acoustic sound features, phonetic representations, and individual words were investigated and decoded from electrocorticographic signals recorded in epileptic patients in three different studies. This recording technique provides high spatiotemporal resolution, via electrodes placed beneath the skull, but without penetrating the cortex In the first study, we reconstructed continuous spectrotemporal acoustic features from brain signals recorded during imagined speech using cross-condition linear regression. Using this technique, we showed that significant acoustic features of imagined speech could be reconstructed in seven patients. In the second study, we decoded continuous phoneme sequences from brain signals recorded during imagined speech using hidden Markov models. This technique allowed incorporating a language model that defined phoneme transitions probabilities. In this preliminary study, decoding accuracy was significant across eight phonemes in one patients. In the third study, we classified individual words from brain signals recorded during an imagined speech word repetition task, using support-vector machines. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the classification framework. Classification accuracy was significant across five patients. In order to compare speech representations across conditions and integrate imagined speech into the general speech network, we investigated imagined speech in parallel with overt speech production and/or speech perception. Results shared across the three studies showed partial overlapping between imagined speech and speech perception/production in speech areas, such as superior temporal lobe, anterior frontal gyrus and sensorimotor cortex. In an attempt to understanding higher-level cognitive processing of auditory processes, we also investigated the neural encoding of acoustic features during music imagery using linear regression. Despite this study was not directly related to speech representations, it provided a unique opportunity to quantitatively study features of inner subjective experiences, similar to speech imagery. These studies demonstrated the potential of using predictive models for basic decoding of speech features. Despite low performance, results show the feasibility for direct decoding of natural speech. In this respect, we highlighted numerous challenges that were encountered, and suggested new avenues to improve performances

Neurophysiological mechanisms of sensorimotor recovery from stroke

Ischemic stroke often results in the devastating loss of nervous tissue in the cerebral cortex, leading to profound motor deficits when motor territory is lost, and ultimately resulting in a substantial reduction in quality of life for the stroke survivor. The International Classification of Functioning, Disability and Health (ICF) was developed in 2002 by the World Health Organization (WHO) and provides a framework for clinically defining impairment after stroke. While the reduction of burdens due to neurological disease is stated as a mission objective of the National Institute of Neurological Disorders and Stroke (NINDS), recent clinical trials have been unsuccessful in translating preclinical research breakthroughs into actionable therapeutic treatment strategies with meaningful progress towards this goal. This means that research expanding another NINDS mission is now more important than ever: improving fundamental knowledge about the brain and nervous system in order to illuminate the way forward. Past work in the monkey model of ischemic stroke has suggested there may be a relationship between motor improvements after injury and the ability of the animal to reintegrate sensory and motor information during behavior. This relationship may be subserved by sprouting cortical axonal processes that originate in the spared premotor cortex after motor cortical injury in squirrel monkeys. The axons were observed to grow for relatively long distances (millimeters), significantly changing direction so that it appears that they specifically navigate around the injury site and reorient toward the spared sensory cortex. Critically, it remains unknown whether such processes ever form functional synapses, and if they do, whether such synapses perform meaningful calculations or other functions during behavior. The intent of this dissertation was to study this phenomenon in both intact rats and rats with a focal ischemia in primary motor cortex (M1) contralateral to the preferred forelimb during a pellet retrieval task. As this proved to be a challenging and resource-intensive endeavor, a primary objective of the dissertation became to provide the tools to facilitate such a project to begin with. This includes the creation of software, hardware, and novel training and behavioral paradigms for the rat model. At the same time, analysis of previous experimental data suggested that plasticity in the neural activity of the bilateral motor cortices of rats performing pellet retrievals after focal M1 ischemia may exhibit its most salient changes with respect to functional changes in behavior via mechanisms that were different than initially hypothesized. Specifically, a major finding of this dissertation is the finding that evidence of plasticity in the unit activity of bilateral motor cortical areas of the reaching rat is much stronger at the level of population features. These features exhibit changes in dynamics that suggest a shift in network fixed points, which may relate to the stability of filtering performed during behavior. It is therefore predicted that in order to define recovery by comparison to restitution, a specific type of fixed point dynamics must be present in the cortical population state. A final suggestion is that the stability or presence of these dynamics is related to the reintegration of sensory information to the cortex, which may relate to the positive impact of physical therapy during rehabilitation in the postacute window. Although many more rats will be needed to state any of these findings as a definitive fact, this line of inquiry appears to be productive for identifying targets related to sensorimotor integration which may enhance the efficacy of future therapeutic strategies

TEMPTED BY THE EYES: BEHAVIORAL AND BRAIN RESPONSES TO FOOD SHAPED BY APPRECIATION, PREFERENCES AND FOOD-EXTRINSIC INFORMATION

Obesity has become a major public health issue as it has reached pandemic proportions over the last decades. This increasing prevalence of obesity and overweight in industrialized countries is to a large part explained by the abundance of tempting foods promoting overeating and subsequent weight gain. Resisting food temptations has thus become a necessity in order to maintain a healthy body weight. The thesis at hand provides a better understanding of behavioral and brain responses involved in sensory food perception, reward and control. The first study (study A) assessed how food liking influences subsequent choice between two food alternatives, and how, in turn, these factors modulate brain responses to the viewing of high- and low- energy foods (published manuscript: “Does my brain want what my eyes like? – How food liking influences choice and impacts spatio-temporal brain dynamics to food viewing" (Bielser & Crézé et al., 2015)). In this study, we found that strongly like foods were chosen more often and faster than less liked foods. Further, the level of liking and subsequent choice influenced brain responses in areas involved in reward attribution as well as decision-making processes, likely influencing prospective food intake. The second study (study B) investigated the neural representation of meal images varying in portion size in the context of prospective food intake and expected satiety (published manuscript: “Brain dynamics of meal selection in humans" (Toepel, Bielser et al., 2015)). In this study, our results showed that brain regions involved in visual processing and reward attribution trace physical portion size increases during early stages of perception, likely reflective of the quantification of the amount of food available for subsequent intake. During a later stage of information processing, brain regions involved in attention and adaptive behaviors responded to "ideal” portion sizes, likely reflecting control over food intake to select portions to achieve adequate satiety. The third study (study C) assessed how encountering traffic light labeling (as used on food packages) preceding food images influenced behavioral and brain responses to high- and low-energy foods (“Biasing behavioral decisions and brain responses to food with traffic light labeling" (Bielser et al., in preparation)). In this study, we found that traffic light labeling and energetic content of viewed foods modulated neural activity in a network of regions known to be involved in reward valuation, inhibitory control, attention and object categorization. These findings support traffic light labeling as a potentially effective means to guide food choices and ameliorate body weight long-term management. Together, the studies comprised in this thesis showed that modulations of neural activity in response to food perception occur already at early stages of visual processing and can be influenced by the level of appreciation, the amount of food presented as well as food-extrinsic information. These findings contribute to a better understanding of factors shaping food-related behavior and, in extension, food intake. -- L’obésité est devenue un problème majeur de santé publique qui a atteint des proportions pandémiques au cours des dernières décennies. L’augmentation de la prévalence du surpoids et de l’obésité dans les pays industrialisés s’explique en grande partie par l’abondance de nourriture dont le degré d’attirance incite à une consommation en excès et engendre une prise de poids. Cette thèse avait pour but une meilleure compréhension des réponses comportementales et cérébrales impliquées dans la perception sensorielle de nourriture, la récompense et le contrôle. La première étude (étude A) a investigué la façon dont l’appréciation de la nourriture influence un choix subséquent entre deux alternatives alimentaires, et comment, par extension, ces facteurs modulent les réponses cérébrales à la vue de nourriture à haute et basse teneur énergétique (manuscrit publié : “Does my brain want what my eyes like? – How food liking influences choice and impacts spatio- temporal brain dynamics to food viewing" (Bielser & Crézé et al., 2015)). Dans cette étude, nous avons montré que la nourriture hautement appréciée est choisie plus souvent que les aliments moins bien notés. De plus, le niveau d’appréciation et le choix subséquent influencent les réponses cérébrales d’aires impliquées dans l’attribution de récompense ainsi que dans les processus de prise de décision et par la même, un impact probable sur la prise alimentaire prospective. La deuxième étude (étude B) a investigué les représentations cérébrales d’images de repas dont la taille des portions varient, dans le contexte d’une prise alimentaire prospective et de la satiété en résultant (manuscrit publié : “Brain dynamics of meal selection in humans" (Toepel, Bielser et al., 2015)). Dans cette étude, nos résultats ont montré que des régions cérébrales impliquées dans les processus visuels, ainsi que dans l’attribution de récompense tracent les augmentations physiques de portion durant les premières étapes de perception, représentant probablement une quantification de la nourriture disponible pour une prise alimentaire subséquente. Durant une étape plus tardive du décodage d’information, des régions cérébrales impliquées dans l’attention et dans les comportements adaptatifs présentent une forte réactivité pour les portions jugées de taille « idéale », reflétant sans doute un contrôle sur la prise alimentaire afin de sélectionner une portion permettant d’atteindre une satiété adéquate. La troisième étude (étude C) a investigué comment la rencontre fortuite de feux de circulation, comme ceux utilisés actuellement sur les labels d’étiquetage alimentaire, influence les réponses comportementales et cérébrales à la vue de nourriture à haute et basse teneur énergétique ("Biasing behavioral decisions and brain responses to food with traffic light labeling" (Bielser et al., en préparation)). Dans cette étude, nous avons montré que ces labels modulent les réponses cérébrales dans un réseau d’aires impliquées dans l’attribution de récompense, le contrôle inhibiteur, l’attention et la catégorisation d’objets. Ces résultats démontrent l’efficacité des labels reproduisant les feux de circulation comme moyen de guidage des choix alimentaires et d’amélioration de la gestion du poids à long terme. Ensemble, les études comprises dans cette thèse ont démontré que les modulations de l’activité cérébrale en réponse à la perception de nourriture ont lieu à des étapes très précoces du décodage d’information visuelle et qu’elles peuvent être influencées par le niveau d’appréciation, la quantité de nourriture disponible ainsi que par des informations contextuelles