Auswahl der wissenschaftlichen Literatur zum Thema „Wearable neurotechnology“

Abstract Introduction The normal transition to sleep is characterized by a reduction in higher frequency activity and an increase in lower frequency activity in frontal brain regions. In sleep onset insomnia these changes in activity are weaker and may prolong the transition to sleep. We performed a translational study to investigate the impact of a new wearable neurotechnology using 0.75 Hz transcranial electrical stimulation (tES) to improve sleep outcomes in a population displaying symptoms of sleep onset insomnia. Methods Participants were enrolled based on a qualifying insomnia severity index score of greater than 7 and an average sleep onset latency of greater than 30 minutes during baseline monitoring. Using a wearable device we compared 30 minutes of 0.75 Hz tES prior to going to bed with an active control at 25 Hz in the same individuals. Sleep behaviors were tracked in the home using a FitBit inspire wrist worn actigraphy device. Sleep onset latency (SOL), time asleep, and wake after sleep onset were measured during one-week baselines and then again over the two, one-week periods of use with the wearable device. Neurophysiological responses to both stimulation frequencies were observed using electroencephalography within the wearable device and correlated with observed differences in sleep behaviors. All outcome measures were compared against pre-stimulation baselines within subjects and then averaged across subjects. Results Treatment with 0.75 Hz consistently reduced SOL by 54% when compared with pre-treatment baselines (p<< 0.001). Stimulation with 25 Hz reduced SOL by 28% (p< 0.05) but displayed order effects suggesting the possibility of placebo. The reduction in SOL with 0.75 Hz treatment was linearly proportional to an individual’s baseline with those individuals with the greatest severity realizing the greatest benefit (r^2 = 0.62, p<< 0.001). Changes in SOL were correlated with left/right coherence around the stimulation frequency of 0.75 Hz (r^2 = 0.33, p< 0.01) providing a possible mechanism. Time asleep was increased by 20 minutes with 0.75 Hz treatment (p< 0.05), and -1 minute with 25 Hz (N.S.). Conclusion Our study provides translational evidence for an exciting new treatment for sleep onset insomnia that is safe, effective and can be delivered in the home. Support (if any)

Advances in neuromodulation technologies hold the promise of treating a patient’s unique brain network pathology using personalized stimulation patterns. In service of these goals, neuromodulation clinical trials using sensing-enabled devices are routinely generating large multi-modal datasets. However, with the expansion of data acquisition also comes an increasing difficulty to store, manage, and analyze the associated datasets, which integrate complex neural and wearable time-series data with dynamic assessments of patients’ symptomatic state. Here, we discuss a scalable cloud-based data platform that enables ingestion, aggregation, storage, query, and analysis of multi-modal neurotechnology datasets. This large-scale data infrastructure will accelerate translational neuromodulation research and enable the development and delivery of next-generation deep brain stimulation therapies.

Novel wearable neurotechnology is able to provide insight into its wearer's cognitive processes and offers ways to change or enhance their capacities. Moreover, it offers the promise of hands-free device control. These brain-computer interfaces are likely to become an everyday technology in the near future, due to their increasing accessibility and affordability. We, therefore, must anticipate their impact, not only on society and individuals broadly but also more specifically on sectors such as traffic and transport. In an economy where attention is increasingly becoming a scarce good, these innovations may present both opportunities and challenges for daily activities that require focus, such as driving and cycling. Here, we argue that their development carries a dual risk. Firstly, BCI-based devices may match or further increase the intensity of cognitive human-technology interaction over the current hands-free communication devices which, despite being widely accepted, are well-known for introducing a significant amount of cognitive load and distraction. Secondly, BCI-based devices will be typically harder than hands-free devices to both visually detect (e.g., how can law enforcement check when these extremely small and well-integrated devices are used?) and restrain in their use (e.g., how do we prevent users from using such neurotechnologies without breaching personal integrity and privacy?). Their use in traffic should be anticipated by researchers, engineers, and policymakers, in order to ensure the safety of all road users.

This paper presents the first garment capable of measuring brain activity with accuracy comparable to that of state-of-the art dry electroencephalogram (EEG) systems. The main innovation is an EEG sensor layer (i.e., the electrodes, the signal transmission, and the cap support) made entirely of threads, fabrics, and smart textiles, eliminating the need for metal or plastic materials. The garment is connected to a mobile EEG amplifier to complete the measurement system. As a first proof of concept, the new EEG system (Garment-EEG) was characterized with respect to a state-of-the-art Ag/AgCl dry-EEG system (Dry-EEG) over the forehead area of healthy participants in terms of: (1) skin-electrode impedance; (2) EEG activity; (3) artifacts; and (4) user ergonomics and comfort. The results show that the Garment-EEG system provides comparable recordings to Dry-EEG, but it is more susceptible to artifacts under adverse recording conditions due to poorer contact impedances. The textile-based sensor layer offers superior ergonomics and comfort compared to its metal-based counterpart. We provide the datasets recorded with Garment-EEG and Dry-EEG systems, making available the first open-access dataset of an EEG sensor layer built exclusively with textile materials. Achieving user acceptance is an obstacle in the field of neurotechnology. The introduction of EEG systems encapsulated in wearables has the potential to democratize neurotechnology and non-invasive brain-computer interfaces, as they are naturally accepted by people in their daily lives. Furthermore, supporting the EEG implementation in the textile industry may result in lower cost and less-polluting manufacturing processes compared to metal and plastic industries.

Au cours des dernières décennies, les avancées révolutionnaires en neuroimagerie ont permis de considérablement améliorer notre compréhension du cerveau. Aujourd'hui, avec la disponibilité croissante des dispositifs personnels de neuroimagerie portables, tels que l'EEG mobile " à bas prix ", une nouvelle ère s’annonce où cette technologie n'est plus limitée aux laboratoires de recherche ou aux contextes cliniques. Les applications de l’EEG dans le " monde réel " présentent cependant leur lot de défis, de la rareté des données étiquetées à la qualité imprévisible des signaux et leur résolution spatiale limitée. Dans cette thèse, nous nous appuyons sur le domaine de l'apprentissage profond afin de transformer cette modalité d'imagerie cérébrale centenaire, purement clinique et axée sur la recherche, en une technologie pratique qui peut bénéficier à l'individu au quotidien. Tout d'abord, nous étudions comment les données d’EEG non étiquetées peuvent être mises à profit via l'apprentissage auto-supervisé pour améliorer la performance d’algorithmes d'apprentissage entraînés sur des tâches cliniques courantes. Nous présentons trois approches auto-supervisées qui s'appuient sur la structure temporelle des données elles-mêmes, plutôt que sur des étiquettes souvent difficiles à obtenir, pour apprendre des représentations pertinentes aux tâches cliniques étudiées. Par le biais d'expériences sur des ensembles de données à grande échelle d'enregistrements de sommeil et d’examens neurologiques, nous démontrons l'importance des représentations apprises, et révélons comment les données non étiquetées peuvent améliorer la performance d’algorithmes dans un scénario semi-supervisé. Ensuite, nous explorons des techniques pouvant assurer la robustesse des réseaux de neurones aux fortes sources de bruit souvent présentes dans l’EEG hors laboratoire. Nous présentons le Filtrage Spatial Dynamique, un mécanisme attentionnel qui permet à un réseau de dynamiquement concentrer son traitement sur les canaux EEG les plus instructifs tout en minimisant l’apport des canaux corrompus. Des expériences sur des ensembles de données à grande échelle, ainsi que des données du monde réel démontrent qu'avec l'EEG à peu de canaux, notre module attentionnel gère mieux la corruption qu'une approche automatisée de traitement du bruit, et que les cartes d'attention prédites reflètent le fonctionnement du réseau de neurones. Enfin, nous explorons l'utilisation d'étiquettes faibles afin de développer un biomarqueur de la santé neurophysiologique à partir d'EEG collecté dans le monde réel. Pour ce faire, nous transposons à ces données d'EEG le principe d'âge cérébral, originellement développé avec l'imagerie par résonance magnétique en laboratoire et en clinique. À travers l'EEG de plus d'un millier d'individus enregistré pendant un exercice d'attention focalisée ou le sommeil nocturne, nous démontrons non seulement que l'âge peut être prédit à partir de l'EEG portable, mais aussi que ces prédictions encodent des informations contenues dans des biomarqueurs de santé cérébrale, mais absentes dans l'âge chronologique. Dans l’ensemble, cette thèse franchit un pas de plus vers l’utilisation de l’EEG pour le suivi neurophysiologique en dehors des contextes de recherche et cliniques traditionnels, et ouvre la porte à de nouvelles applications plus flexibles de cette technologie
Our understanding of the brain has improved considerably in the last decades, thanks to groundbreaking advances in the field of neuroimaging. Now, with the invention and wider availability of personal wearable neuroimaging devices, such as low-cost mobile EEG, we have entered an era in which neuroimaging is no longer constrained to traditional research labs or clinics. "Real-world'' EEG comes with its own set of challenges, though, ranging from a scarcity of labelled data to unpredictable signal quality and limited spatial resolution. In this thesis, we draw on the field of deep learning to help transform this century-old brain imaging modality from a purely clinical- and research-focused tool, to a practical technology that can benefit individuals in their day-to-day life. First, we study how unlabelled EEG data can be utilized to gain insights and improve performance on common clinical learning tasks using self-supervised learning. We present three such self-supervised approaches that rely on the temporal structure of the data itself, rather than onerously collected labels, to learn clinically-relevant representations. Through experiments on large-scale datasets of sleep and neurological screening recordings, we demonstrate the significance of the learned representations, and show how unlabelled data can help boost performance in a semi-supervised scenario. Next, we explore ways to ensure neural networks are robust to the strong sources of noise often found in out-of-the-lab EEG recordings. Specifically, we present Dynamic Spatial Filtering, an attention mechanism module that allows a network to dynamically focus its processing on the most informative EEG channels while de-emphasizing any corrupted ones. Experiments on large-scale datasets and real-world data demonstrate that, on sparse EEG, the proposed attention block handles strong corruption better than an automated noise handling approach, and that the predicted attention maps can be interpreted to inspect the functioning of the neural network. Finally, we investigate how weak labels can be used to develop a biomarker of neurophysiological health from real-world EEG. We translate the brain age framework, originally developed using lab and clinic-based magnetic resonance imaging, to real-world EEG data. Using recordings from more than a thousand individuals performing a focused attention exercise or sleeping overnight, we show not only that age can be predicted from wearable EEG, but also that age predictions encode information contained in well-known brain health biomarkers, but not in chronological age. Overall, this thesis brings us a step closer to harnessing EEG for neurophysiological monitoring outside of traditional research and clinical contexts, and opens the door to new and more flexible applications of this technology