167 research outputs found
Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems
International audienc
Towards Accurate Forecasting of Epileptic Seizures: Artificial Intelligence and Effective Connectivity Findings
LâĂ©pilepsie est une des maladies neurologiques les plus frĂ©quentes, touchant prĂšs dâun
pourcent de la population mondiale. De nos jours, bien quâenviron deux tiers des patients
Ă©pileptiques rĂ©pondent adĂ©quatement aux traitements pharmacologiques, il reste quâun tiers des
patients doivent vivre avec des crises invalidantes et imprévisibles. Quoique la chirurgie
dâĂ©pilepsie puisse ĂȘtre une autre option thĂ©rapeutique envisageable, le recours Ă la chirurgie de
résection demeure trÚs faible en partie pour des raisons diverses (taux de réussite modeste, peur
des complications, perceptions nĂ©gatives). Dâautres avenues de traitement sont donc souhaitables.
Une piste actuellement explorĂ©e par des groupes de chercheurs est de tenter de prĂ©dire les crises Ă
partir dâenregistrements de lâactivitĂ© cĂ©rĂ©brale des patients. La capacitĂ© de prĂ©dire la survenue de
crises permettrait notamment aux patients, aidants naturels ou personnels médical de prendre des
mesures de prĂ©caution pour Ă©viter les dĂ©sagrĂ©ments reliĂ©s aux crises voire mĂȘme instaurer un
traitement pour les faire avorter. Au cours des derniĂšres annĂ©es, dâimportants efforts ont Ă©tĂ©
dĂ©ployĂ©s pour dĂ©velopper des algorithmes de prĂ©diction de crises et dâen amĂ©liorer les
performances.
Toutefois, le manque dâenregistrements Ă©lectroencĂ©phalographiques intracrĂąniens (iEEG) de
longue durée de qualité, la quantité limitée de crises, ainsi que la courte durée des périodes
interictales constituaient des obstacles majeurs à une évaluation adéquate de la performance des
algorithmes de prĂ©diction de crises. RĂ©cemment, la disponibilitĂ© en ligne dâenregistrements iEEG
continus avec échantillonnage bilatéral (des deux hémisphÚres) acquis chez des chiens atteints
dâĂ©pilepsie focale Ă lâaide du dispositif de surveillance ambulatoire implantable NeuroVista a
partiellement facilitĂ© cette tĂąche. Cependant, une des limitations associĂ©es Ă lâutilisation de ces
donnĂ©es durant la conception dâun algorithme de prĂ©diction de crises Ă©tait lâabsence
dâinformation concernant la zone exacte de dĂ©but des crises (information non fournie par les
gestionnaires de cette base de données en ligne). Le premier objectif de cette thÚse était la mise
en oeuvre dâun algorithme prĂ©cis de prĂ©diction de crises basĂ© sur des enregistrements iEEG canins
de longue durée. Les principales contributions à cet égard incluent une localisation quantitative
de la zone dâapparition des crises (basĂ©e sur la fonction de transfert dirigĂ© âDTF), lâutilisation
dâune nouvelle fonction de coĂ»t via lâalgorithme gĂ©nĂ©tique proposĂ©, ainsi quâune Ă©valuation
quasi-prospective des performances de prĂ©diction (donnĂ©es de test dâun total de 893 jours). Les rĂ©sultats ont montrĂ© une amĂ©lioration des performances de prĂ©diction par rapport aux Ă©tudes
antérieures, atteignant une sensibilité moyenne de 84.82 % et un temps en avertissement de 10 %.
La DTF, utilisée précédemment comme mesure de connectivité pour déterminer le réseau
épileptique (objectif 1), a été préalablement validée pour quantifier les relations causales entre les
canaux lorsque les exigences de quasi-stationnarité sont satisfaites. Ceci est possible dans le cas
des enregistrements canins en raison du nombre relativement faible de canaux. Pour faire face
aux exigences de non-stationnarité, la fonction de transfert adaptatif pondérée par le spectre
(Spectrum weighted adaptive directed transfer function - swADTF) a Ă©tĂ© introduit en tant quâune
version variant dans le temps de la DTF. Le second objectif de cette thĂšse Ă©tait de valider la
possibilitĂ© dâidentifier les endroits Ă©metteurs (ou sources) et rĂ©cepteurs dâactivitĂ© Ă©pileptiques en
appliquant la swADTF sur des enregistrements iEEG de haute densité provenant de patients
admis pour Ă©valuation prĂ©-chirurgicale au CHUM. Les gĂ©nĂ©rateurs dâactivitĂ© Ă©pileptique Ă©taient
dans le volume réséqué pour les patients ayant des bons résultats post-chirurgicaux alors que
différents foyers ont été identifiés chez les patients ayant eu de mauvais résultats postchirurgicaux.
Ces rĂ©sultats dĂ©montrent la possibilitĂ© dâune identification prĂ©cise des sources et
rĂ©cepteurs dâactivitĂ©s Ă©pileptiques au moyen de la swADTF ouvrant la porte Ă la possibilitĂ© dâune
meilleure sĂ©lection dâĂ©lectrodes de maniĂšre quantitative dans un contexte de dĂ©veloppement
dâalgorithme de prĂ©diction de crises chez lâhumain.
Dans le but dâexplorer de nouvelles avenues pour la prĂ©diction de crises Ă©pileptiques, un
nouveau prĂ©curseur a aussi Ă©tĂ© Ă©tudiĂ© combinant lâanalyse des spectres dâordre supĂ©rieur et les
réseaux de neurones artificiels (objectif 3). Les résultats ont montré des différences
statistiquement significatives (p<0.05) entre lâĂ©tat prĂ©ictal et lâĂ©tat interictal en utilisant chacune
des caractéristiques extraites du bi-spectre. Utilisées comme entrées à un perceptron multicouche,
lâentropie bispectrale normalisĂ©e, lâentropie carrĂ© normalisĂ©e, et la moyenne ont atteint des
précisions respectives de 78.11 %, 72.64% et 73.26%.
Les résultats de cette thÚse confirment la faisabilité de prédiction de crises à partir
dâenregistrements dâĂ©lectroencĂ©phalographie intracrĂąniens. Cependant, des efforts
supplĂ©mentaires en termes de sĂ©lection dâĂ©lectrodes, dâextraction de caractĂ©ristiques, dâutilisation
des techniques dâapprentissage profond et dâimplĂ©mentation Hardware, sont nĂ©cessaires avant
lâintĂ©gration de ces approches dans les dispositifs implantables commerciaux.----------ABSTRACT
Epilepsy is a chronic condition characterized by recurrent âunpredictableâ seizures. While
the first line of treatment consists of long-term drug therapy about one-third of patients are said to
be pharmacoresistant. In addition, recourse to epilepsy surgery remains low in part due to
persisting negative attitudes towards resective surgery, fear of complications and only moderate
success rates. An important direction of research is to investigate the possibility of predicting
seizures which, if achieved, can lead to novel interventional avenues.
The paucity of intracranial electroencephalography (iEEG) recordings, the limited number of
ictal events, and the short duration of interictal periods have been important obstacles for an
adequate assessment of seizure forecasting. More recently, long-term continuous bilateral iEEG
recordings acquired from dogs with naturally occurring focal epilepsy, using the implantable
NeuroVista ambulatory monitoring device have been made available on line for the benefit of
researchers. Still, an important limitation of these recordings for seizure-prediction studies was
that the seizure onset zone was not disclosed/available. The first objective of this thesis was to
develop an accurate seizure forecasting algorithm based on these canine ambulatory iEEG
recordings. Main contributions include a quantitative, directed transfer function (DTF)-based,
localization of the seizure onset zone (electrode selection), a new fitness function for the
proposed genetic algorithm (feature selection), and a quasi-prospective assessment of seizure
forecasting on long-term continuous iEEG recordings (total of 893 testing days). Results showed
performance improvement compared to previous studies, achieving an average sensitivity of
84.82% and a time in warning of 10 %.
The DTF has been previously validated for quantifying causal relations when quasistationarity
requirements are met. Although such requirements can be fulfilled in the case of
canine recordings due to the relatively low number of channels (objective 1), the identification of
stationary segments would be more challenging in the case of high density iEEG recordings. To
cope with non-stationarity issues, the spectrum weighted adaptive directed transfer function
(swADTF) was recently introduced as a time-varying version of the DTF. The second objective
of this thesis was to validate the feasibility of identifying sources and sinks of seizure activity
based on the swADTF using high-density iEEG recordings of patients admitted for pre-surgical monitoring at the CHUM. Generators of seizure activity were within the resected volume for
patients with good post-surgical outcomes, whereas different or additional seizure foci were
identified in patients with poor post-surgical outcomes. Results confirmed the possibility of
accurate identification of seizure origin and propagation by means of swADTF paving the way
for its use in seizure prediction algorithms by allowing a more tailored electrode selection.
Finally, in an attempt to explore new avenues for seizure forecasting, we proposed a new
precursor of seizure activity by combining higher order spectral analysis and artificial neural
networks (objective 3). Results showed statistically significant differences (p<0.05) between
preictal and interictal states using all the bispectrum-extracted features. Normalized bispectral
entropy, normalized squared entropy and mean of magnitude, when employed as inputs to a
multi-layer perceptron classifier, achieved held-out test accuracies of 78.11%, 72.64%, and
73.26%, respectively.
Results of this thesis confirm the feasibility of seizure forecasting based on iEEG recordings;
the transition into the ictal state is not random and consists of a âbuild-upâ, leading to seizures.
However, additional efforts in terms of electrode selection, feature extraction, hardware and deep
learning implementation, are required before the translation of current approaches into
commercial devices
Magnetoencephalography
This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician
Statistical causality in the EEG for the study of cognitive functions in healthy and pathological brains
Understanding brain functions requires not only information about the spatial localization of neural activity, but also about the dynamic functional links between the involved groups of neurons, which do not work in an isolated way, but rather interact together through ingoing and outgoing connections. The work carried on during the three years of PhD course returns a methodological framework for the estimation of the causal brain connectivity and its validation on simulated and real datasets (EEG and pseudo-EEG) at scalp and source level. Important open issues like the selection of the best algorithms for the source reconstruction and for time-varying estimates were addressed. Moreover, after the application of such approaches on real datasets recorded from healthy subjects and post-stroke patients, we extracted neurophysiological indices describing in a stable and reliable way the properties of the brain circuits underlying different cognitive states in humans (attention, memory). More in detail: I defined and implemented a toolbox (SEED-G toolbox) able to provide a useful validation instrument addressed to researchers who conduct their activity in the field of brain connectivity estimation. It may have strong implication, especially in methodological advancements. It allows to test the ability of different estimators in increasingly less ideal conditions: low number of available samples and trials, high inter-trial variability (very realistic situations when patients are involved in protocols) or, again, time varying connectivity patterns to be estimate (where stationary hypothesis in wide sense failed). A first simulation study demonstrated the robustness and the accuracy of the PDC with respect to the inter-trials variability under a large range of conditions usually encountered in practice. The simulations carried on the time-varying algorithms allowed to highlight the performance of the existing methodologies in different conditions of signals amount and number of available trials. Moreover, the adaptation of the Kalman based algorithm (GLKF) I implemented, with the introduction of the preliminary estimation of the initial conditions for the algorithm, lead to significantly better performance. Another simulation study allowed to identify a tool combining source localization approaches and brain connectivity estimation able to provide accurate and reliable estimates as less as possible affected to the presence of spurious links due to the head volume conduction. The developed and tested methodologies were successfully applied on three real datasets. The first one was recorded from a group of healthy subjects performing an attention task that allowed to describe the brain circuit at scalp and source level related with three important attention functions: alerting, orienting and executive control. The second EEG dataset come from a group of healthy subjects performing a memory task. Also in this case, the approaches under investigation allowed to identify synthetic connectivity-based descriptors able to characterize the three main memory phases (encoding, storage and retrieval). For the last analysis I recorded EEG data from a group of stroke patients performing the same memory task before and after one month of cognitive rehabilitation. The promising results of this preliminary study showed the possibility to follow the changes observed at behavioural level by means of the introduced neurophysiological indices
Bayesian Modeling and Estimation Techniques for the Analysis of Neuroimaging Data
Brain function is hallmarked by its adaptivity and robustness, arising from underlying neural activity that admits well-structured representations in the temporal, spatial, or spectral domains. While neuroimaging techniques such as Electroencephalography (EEG) and magnetoencephalography (MEG) can record rapid neural dynamics at high temporal resolutions, they face several signal processing challenges that hinder their full utilization in capturing these characteristics of neural activity. The objective of this dissertation is to devise statistical modeling and estimation methodologies that account for the dynamic and structured representations of neural activity and to demonstrate their utility in application to experimentally-recorded data.
The first part of this dissertation concerns spectral analysis of neural data. In order to capture the non-stationarities involved in neural oscillations, we integrate multitaper spectral analysis and state-space modeling in a Bayesian estimation setting. We also present a multitaper spectral analysis method tailored for spike trains that captures the non-linearities involved in neuronal spiking. We apply our proposed algorithms to both EEG and spike recordings, which reveal significant gains in spectral resolution and noise reduction.
In the second part, we investigate cortical encoding of speech as manifested in MEG responses. These responses are often modeled via a linear filter, referred to as the temporal response function (TRF). While the TRFs estimated from the sensor-level MEG data have been widely studied, their cortical origins are not fully understood. We define the new notion of Neuro-Current Response Functions (NCRFs) for simultaneously determining the TRFs and their cortical distribution. We develop an efficient algorithm for NCRF estimation and apply it to MEG data, which provides new insights into the cortical dynamics underlying speech processing.
Finally, in the third part, we consider the inference of Granger causal (GC) influences in high-dimensional time series models with sparse coupling. We consider a canonical sparse bivariate autoregressive model and define a new statistic for inferring GC influences, which we refer to as the LASSO-based Granger Causal (LGC) statistic. We establish non-asymptotic guarantees for robust identification of GC influences via the LGC statistic. Applications to simulated and real data demonstrate the utility of the LGC statistic in robust GC identification
Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications
This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brainâcomputer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the userâs emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to âthink outside the labâ. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments
Applications of multi-way analysis for characterizing paediatric electroencephalogram (EEG) recordings
This doctoral thesis outlines advances in multi-way analysis for characterizing
electroencephalogram (EEG) recordings from a paediatric population, with the aim to
describe new links between EEG data and changes in the brain. This entails establishing the
validity of multi-way analysis as a framework for identifying developmental information at the
individual and collective level. Multi-way analysis broadens matrix analysis to a multi-linear
algebraic architecture to identify latent structural relationships in naturally occurring higher
order (n-way) data, like EEG. We use the canonical polyadic decomposition (CPD) as a
multi-way model to efficiently express the complex structures present in paediatric EEG
recordings as unique combinations of low-rank matrices, offering new insights into child
development. This multi-way CPD framework is explored for both typically developing (TD)
children and children with potential developmental delays (DD), e.g. children who suffer from
epilepsy or paediatric stroke.
Resting-state EEG (rEEG) data serves as an intuitive starting point in analyzing paediatric
EEG via multi-way analysis. Here, the CPD model probes the underlying relationships
between the spatial, spectral and subject modes of several rEEG datasets. We demonstrate the
CPD can reveal distinct population-level features in rEEG that reflect unique developmental
traits in varying child populations. These development-affiliated profiles are evaluated with
respect to capturing structures well-established in childhood EEG. The identified features are
also interrogated for their predictive abilities in anticipating new subjectsâ ages. Assessing
simulations and real rEEG datasets of TD and DD children establishes the multi-way analysis
framework as well suited for identifying developmental profiles from paediatric rEEG.
We extend the multi-way analysis scheme to more complex EEG scenarios common in
EEG rehabilitation technology, like brain-computer interfaces. We explore the feasibility of
multi-way modelling for interventions where developmental changes often pose as barriers.
The multi-way CPD model is expanded to include four modes- task, spatial, spectral and
subject data, with non-negativity and orthogonality constraints imposed. We analyze a visual
attention task that elucidates a steady-state visual evoked potential and present the advantages
gained from the extended CPD model. Through direct multi-linear projection, we demonstrate
that linear profiles of the CPD can be capitalized upon for rapid task classification sans
individual subject classifier calibration.
Incorporating concepts from the multi-way analysis scheme with child development measured
by psychometric tests, we propose the Joint EEG Development Inference (JEDI) model for
inferring development from paediatric EEG. We utilize a common EEG task (button-press) to
establish a 4-way CPD model of paediatric EEG data. Structured data fusion of the CPD model
and cognitive scores from psychometric evaluations then permits joint decomposition of the
two datasets to identify common features associated with each representation of development.
Use of grid search optimization and a fully cross-validated design supports the JEDI model as
another technique for rapidly discerning the developmental status of a child via EEG.
We then briefly turn our attention to associating child development as measured by
psychometric tests to markers in the EEG using graph network properties. Using graph
networks, we show how the functional connectivity can inform on potential developmental
delays in very young epileptic children using routine, clinical rEEG measures. This establishes
a potential tool complementary to the JEDI model for identifying and inferring links between
the established psychometric evaluation of developing children and functional analysis of the
EEG.
Multi-way analysis of paediatric EEG data offers a new approach for handling the
developmental status and profiles of children. The CPD model offers flexibility in terms of
identifying development-related features, and can be integrated into EEG tasks common in
rehabilitation paradigms. We aim for the multi-way framework and associated techniques
pursued in this thesis to be integrated and adopted as a useful tool clinicians can use for
characterizing paediatric development
Interpretable Convolutional Neural Networks for Decoding and Analyzing Neural Time Series Data
Machine learning is widely adopted to decode multi-variate neural time series, including electroencephalographic (EEG) and single-cell recordings. Recent solutions based on deep learning (DL) outperformed traditional decoders by automatically extracting relevant discriminative features from raw or minimally pre-processed signals.
Convolutional Neural Networks (CNNs) have been successfully applied to EEG and are the most common DL-based EEG decoders in the state-of-the-art (SOA). However, the current research is affected by some limitations. SOA CNNs for EEG decoding usually exploit deep and heavy structures with the risk of overfitting small datasets, and architectures are often defined empirically. Furthermore, CNNs are mainly validated by designing within-subject decoders. Crucially, the automatically learned features mainly remain unexplored; conversely, interpreting these features may be of great value to use decoders also as analysis tools, highlighting neural signatures underlying the different decoded brain or behavioral states in a data-driven way. Lastly, SOA DL-based algorithms used to decode single-cell recordings rely on more complex, slower to train and less interpretable networks than CNNs, and the use of CNNs with these signals has not been investigated.
This PhD research addresses the previous limitations, with reference to P300 and motor decoding from EEG, and motor decoding from single-neuron activity. CNNs were designed light, compact, and interpretable. Moreover, multiple training strategies were adopted, including transfer learning, which could reduce training times promoting the application of CNNs in practice. Furthermore, CNN-based EEG analyses were proposed to study neural features in the spatial, temporal and frequency domains, and proved to better highlight and enhance relevant neural features related to P300 and motor states than canonical EEG analyses. Remarkably, these analyses could be used, in perspective, to design novel EEG biomarkers for neurological or neurodevelopmental disorders. Lastly, CNNs were developed to decode single-neuron activity, providing a better compromise between performance and model complexity
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