Literatura científica selecionada sobre o tema "Wearable neurotechnology"
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Artigos de revistas sobre o assunto "Wearable neurotechnology"
Simons, Stephen, Maria Provo e Renee Shimizu. "0431 Transcranial Electrical Stimulation with a Wearable Device Dramatically Reduces Sleep Onset Latency in Insomnia". SLEEP 47, Supplement_1 (20 de abril de 2024): A185. http://dx.doi.org/10.1093/sleep/zsae067.0431.
Texto completo da fontedu Bois, N., A. D. Bigirimana, A. Korik, L. Gaju Kéthina, E. Rutembesa, J. Mutabaruka, L. Mutesa, G. Prasad, S. Jansen e D. Coyle. "Electroencephalography and psychological assessment datasets to determine the efficacy of a low-cost, wearable neurotechnology intervention for reducing Post-Traumatic Stress Disorder symptom severity". Data in Brief 42 (junho de 2022): 108066. http://dx.doi.org/10.1016/j.dib.2022.108066.
Texto completo da fonteChen, Witney, Lowry Kirkby, Miro Kotzev, Patrick Song, Ro’ee Gilron e Brian Pepin. "The Role of Large-Scale Data Infrastructure in Developing Next-Generation Deep Brain Stimulation Therapies". Frontiers in Human Neuroscience 15 (6 de setembro de 2021). http://dx.doi.org/10.3389/fnhum.2021.717401.
Texto completo da fonteBosch, Victoria, e Giulio Mecacci. "Eyes on the road: brain computer interfaces and cognitive distraction in traffic". Frontiers in Neuroergonomics 4 (26 de maio de 2023). http://dx.doi.org/10.3389/fnrgo.2023.1171910.
Texto completo da fonteLópez-Larraz, Eduardo, Carlos Escolano, Almudena Robledo-Menéndez, Leyre Morlas, Alexandra Alda e Javier Minguez. "A garment that measures brain activity: proof of concept of an EEG sensor layer fully implemented with smart textiles". Frontiers in Human Neuroscience 17 (26 de maio de 2023). http://dx.doi.org/10.3389/fnhum.2023.1135153.
Texto completo da fonteTeses / dissertações sobre o assunto "Wearable neurotechnology"
Banville, Hubert. "Enabling real-world EEG applications with deep learning". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG005.
Texto completo da fonteOur 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
Trabalhos de conferências sobre o assunto "Wearable neurotechnology"
Elmalaki, Salma, Berken Utku Demirel, Mojtaba Taherisadr, Sara Stern-Nezer, Jack J. Lin e Mohammad Abdullah Al Faruque. "Towards Internet-of-Things for Wearable Neurotechnology". In 2021 22nd International Symposium on Quality Electronic Design (ISQED). IEEE, 2021. http://dx.doi.org/10.1109/isqed51717.2021.9424364.
Texto completo da fonteRubio Ballester, Belén, Alica Lathe, Esther Duarte, Armin Duff e Paul F. M. J. Verschure. "A Wearable Bracelet Device for Promoting Arm Use in Stroke Patients". In International Congress on Neurotechnology, Electronics and Informatics. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005662300240031.
Texto completo da fonte