Electroencephalography is a medical diagnosis technique. It consists in measuring the biopotentials produced by the upper layers of the brain at various standardized places on the skull.<p><p>Since the biopotentials produced by the upper parts of the brain have an amplitude of about one microvolt, the measurements performed by an EEG are exposed to many risks.<p><p>Moreover, since the present tendency is measure those signals over periods of several hours, or even several days, human analysis of the recording becomes extremely long and difficult. The use of signal analysis techniques for the help of paroxysm detection with clinical interest within the electroencephalogram becomes therefore almost essential. However the performance of many automatic detection algorithms becomes significantly degraded by the presence of interference: the quality of the recordings is therefore fundamental. <p><p>This thesis explores the benefits that electronics and signal processing could bring to electroencephalography, aiming at improving the signal quality and semi-automating the data processing.<p><p>These two aspects are interdependent because the performance of any semi-automation of the data processing depends on the quality of the acquired signal. Special attention is focused on the interaction between these two goals and attaining the optimal hardware/software pair. <p><p>This thesis offers an overview of the medical electroencephalographic acquisition chain and also of its possible improvements.<p> <p>The conclusions of this work may be extended to some other cases of biological signal amplification such as the electrocardiogram (ECG) and the electromyogram (EMG). Moreover, such a generalization would be easier, because their signals have a wider amplitude and are therefore more resistant toward interference.<p> / Doctorat en sciences appliquées / info:eu-repo/semantics/nonPublished

Objective. Dry electrodes have an advantage over gel-based 'wet' electrodes by providing quicker set-up time for electroencephalography recording; however, the potentially poorer contact can result in noisier recordings. We examine the impact that this may have on brain-computer interface communication and potential approaches for mitigation. Approach. We present a performance comparison of wet and dry electrodes for use with the P300 speller system in both healthy participants and participants with communication disabilities (ALS and PLS), and investigate the potential for a data-driven dynamic data collection algorithm to compensate for the lower signal-to-noise ratio (SNR) in dry systems. Main results. Performance results from sixteen healthy participants obtained in the standard static data collection environment demonstrate a substantial loss in accuracy with the dry system. Using a dynamic stopping algorithm, performance may have been improved by collecting more data in the dry system for ten healthy participants and eight participants with communication disabilities; however, the algorithm did not fully compensate for the lower SNR of the dry system. An analysis of the wet and dry system recordings revealed that delta and theta frequency band power (0.1-4 Hz and 4-8 Hz, respectively) are consistently higher in dry system recordings across participants, indicating that transient and drift artifacts may be an issue for dry systems. Significance. Using dry electrodes is desirable for reduced set-up time; however, this study demonstrates that online performance is significantly poorer than for wet electrodes for users with and without disabilities. We test a new application of dynamic stopping algorithms to compensate for poorer SNR. Dynamic stopping improved dry system performance; however, further signal processing efforts are likely necessary for full mitigation.

Event-related brain potentials (ERPs) in electroencephalography are manifestations at the scalp of neural activity that is triggered by, and is involved in, the processing of specific events. This chapter focuses on braincomputer interfaces (BCIs) that use P300, an endogenous ERP component. The P300 is a positive potential that occurs over central-parietal scalp 250- 700 msec after a rare event occurs in the context of the oddball paradigm. This paradigm has three essential attributes: a subject is presented with a series of events (i.e., stimuli), each of which falls into one of two classes; the events that fall into one of the classes are less frequent than those that fall into the other class; and the subject performs a task that requires classifying each event into one of the two classes.

Le sommeil est un phénomène biologique universel complexe et encore peu compris. La méthode de référence actuelle pour caractériser les états de vigilance au cours du sommeil est la polysomnographie (PSG) qui enregistre de manière non invasive à la surface de la peau, les modifications électrophysiologiques de l’activité cérébrale (électroencéphalographie, EEG), oculaire (électro-oculographie, EOG) et musculaire (électromyographie, EMG). Traditionnellement, les signaux électrophysiologiques sont ensuite analysés par un expert du sommeil qui annote manuellement les évènements d’intérêt comme les stades de sommeil ou certains micro-évènements (grapho éléments EEG). Toutefois, l’annotation manuelle est chronophage et sujette à la subjectivité de l’expert. De plus, le développement exponentiel d’outils de monitoring du sommeil enregistrant et analysant automatiquement les signaux électrophysiologiques tels que le bandeau Dreem rend nécessaire une automatisation de ces tâches.L’apprentissage machine bénéficie d’une attention croissante car il permet d’apprendre à un ordinateur à réaliser certaines tâches de décision à partir d’un ensemble d’exemples d’apprentissage et d’obtenir des performances de prédictions plus élevées qu’avec les méthodes classiques. Les avancées techniques dans le domaine de l’apprentissage profond ont ouvert de nouvelles perspectives pour la science du sommeil tout en soulevant de nouveaux défis techniques. L’entraînement des algorithmes d’apprentissage profond nécessite une grande quantité de données annotées qui n’est pas nécessairement disponible pour les données PSG. De plus, les algorithmes d’apprentissage sont très sensibles à la variabilité des données qui est non négligeable en ce qui concerne ces données. Cela s’explique par la variabilité intra et inter-sujet (pathologies / sujets sains, âge…).Cette thèse étudie le développement d’algorithmes d’apprentissage profond afin de réaliser deux types de tâches: la prédiction des stades de sommeil et la détection de micro-événements. Une attention particulière est portée (a) sur la quantité de données annotées requise pour l’entraînement des algorithmes proposés et (b) sur la sensibilité de ces algorithmes à la variabilité des données. Des stratégies spécifiques, basées sur l’apprentissage par transfert, sont proposées pour résoudre les problèmes techniques dus au manque de données annotées et à la variabilité des données. / Sleep is a complex and not fully understood biological phenomenon. The traditional process to monitor sleep relies on the polysomnography exam (PSG). It records, in a non invasive fashion at the level of the skin, electrophysiological modifications of the brain activity (electroencephalography, EEG), ocular (electro-oculography, EOG) and muscular (electro-myography, EMG). The recorded signals are then analyzed by a sleep expert who manually annotates the events of interest such as the sleep stages or some micro-events. However, manual labeling is time-consuming and prone to the expert subjectivity. Furthermore, the development of sleep monitoring consumer wearable devices which record and process automatically electrophysiological signals, such as Dreem headband, requires to automate some labeling tasks.Machine learning (ML) has received much attention as a way to teach a computer to perform some decision tasks automatically from a set of learning examples. Furthermore, the rise of deep learning (DL) algorithms in several fields have opened new perspectives for sleep sciences. On the other hand, this is also raising new concerns related to the scarcity of labeled data that may prevent their training processes and the variability of data that may hurt their performances. Indeed, sleep data is scarce due to the labeling burden and exhibits also some intra and inter-subject variability (due to sleep disorders, aging...).This thesis has investigated and proposed ML algorithms to automate the detection of sleep related events from raw PSG time series. Through the prism of DL, it addressed two main tasks: sleep stage classification and micro-event detection. A particular attention was brought (a) to the quantity of labeled data required to train such algorithms and (b) to the generalization performances of these algorithms to new (variable) data. Specific strategies, based on transfer learning, were designed to cope with the issues related to the scarcity of labeled data and the variability of data.

The use of electroencephalogram (EEG), an electrophysiological monitoring method for recording the brain activity, for authentication has attracted the interest of researchers for over a decade. In addition to exhibiting qualities of biometric-based authentication, they are revocable, impossible to mimic, and resistant to coercion attacks. However, EEG signals carry a wealth of information about an individual and can reveal private information about the user. This brings significant privacy issues to EEG-based authentication systems as they have access to raw EEG signals. This thesis proposes a privacy-preserving EEG-based authentication system that preserves the privacy of the user by not revealing the raw EEG signals while allowing the system to authenticate the user accurately. In that, perceptual hashing is utilized and instead of raw EEG signals, their perceptually hashed values are used in the authentication process. In addition to describing the authentication process, algorithms to compute the perceptual hash are developed based on two feature extraction techniques. Experimental results show that an authentication system using perceptual hashing can achieve performance comparable to a system that has access to raw EEG signals if enough EEG channels are used in the process. This thesis also presents a security analysis to show that perceptual hashing can prevent information leakage.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19126号 / 情博第572号 / 新制||情||100(附属図書館) / 32077 / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 石井 信, 教授 田中 利幸, 教授 加納 学 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

京都大学 / 0048 / 新制・論文博士 / 博士(医学) / 乙第13125号 / 論医博第2134号 / 新制||医||1024(附属図書館) / (主査)教授 宮本 享, 教授 村井 俊哉, 教授 井上 治久 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

The human brain relies on the complex interactions of distinct brain regions to support cognitive processes. The interplay between the hippocampus and neocortical regions plays a key role in the formation, storage, and retrieval of long-term episodic memories. Oscillatory activities during sleep are a fundamental mechanism that binds distributed neuronal networks into functionally coherent ensembles. However, the large-scale hippocampal-neocortical oscillatory mechanisms that support flexible modulation of long-term memory remain poorly understood. Furthermore, alterations to physiologic spatiotemporal patterns that are essential for intact memory function can result in pathophysiology in brain disorders such as focal epilepsy. Investigating how epileptic network activity disrupts connectivity in distributed networks and the organization of oscillatory activity are additional crucial areas that require further research. Our experiments on rodents and human patients with epilepsy have provided valuable insights into these mechanisms. In rodents, we used high-density microelectrode arrays in tandem with hippocampal probes to analyze intracranial electroencephalography (iEEG) from multiple cortical regions and the hippocampus. We identified key hippocampal-cortical oscillatory biomarkers that were differentially coordinated based on the age, strength, and type of memory. We also analyzed iEEG from patients with focal epilepsy and demonstrated how individualized pattern of pathologic-physiologic coupling can disrupt large-scale memory circuits. Our findings may offer new opportunities for therapies aimed at addressing distributed network dysfunction in various neuropsychiatric disorders.

Objective: To assess the reliability and usefulness of an EEG-based brain-computer interface (BCI) for patients with advanced amyotrophic lateral sclerosis (ALS) who used it independently at home for up to 18 months. Methods: Of 42 patients consented, 39 (93%) met the study criteria, and 37 (88%) were assessed for use of the Wadsworth BCI. Nine (21%) could not use the BCI. Of the other 28, 27 (men, age 28-79 years) (64%) had the BCI placed in their homes, and they and their caregivers were trained to use it. Use data were collected by Internet. Periodic visits evaluated BCI benefit and burden and quality of life. Results: Over subsequent months, 12 (29% of the original 42) left the study because of death or rapid disease progression and 6 (14%) left because of decreased interest. Fourteen (33%) completed training and used the BCI independently, mainly for communication. Technical problems were rare. Patient and caregiver ratings indicated that BCI benefit exceeded burden. Quality of life remained stable. Of those not lost to the disease, half completed the study; all but 1 patient kept the BCI for further use. Conclusion: The Wadsworth BCI home system can function reliably and usefully when operated by patients in their homes. BCIs that support communication are at present most suitable for people who are severely disabled but are otherwise in stable health. Improvements in BCI convenience and performance, including some now underway, should increase the number of people who find them useful and the extent to which they are used.

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