Dominik Klepl
@DominikKlepl
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Network neuroscience, graph theory, machine learning PhD Student at Coventry University
Coventry, UK
Joined April 2014
🧠 Exploring the brain's mysteries! A newly published survey provides a detailed review of different GNN models used in recent EEG-based research. Find it in the latest edition of IEEE TNSRE at https://t.co/3fUTHOtAVy.
#neuroscience #humanbrain #brainscience #neurological
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New preprint: our survey paper on GNN-based EEG classification is out. Another excellent piece of work from @DominikKlepl. @CovUni_CSM
https://t.co/7Wn4r7SyL1
arxiv.org
Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been...
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Our new paper is out with OA: Cross-Frequency Multilayer Network Analysis with Bispectrum-based Functional Connectivity: A Study of Alzheimer’s Disease. Credit to @DominikKlepl
#Alzheimers #EEG #network @CovUni_CSM
https://t.co/LsxAnjlBCR
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Interested in nonlinear dynamical system, brain network, EEG and dementia? - I have a fully-funded PhD open for Jan 2024. Send me your CV first please. (the candidate will work alongside a PDRA on my recent EPSRC grant) @CovUni_CSM @CovUniResearch
https://t.co/dtmrs3piJf
findaphd.com
PhD Project - Nonlinear system identification and deep learning for neurodegenerative disease detection at Coventry University, listed on FindAPhD.com
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Just wrapped up our first-ever PGR conference where students presented their research! A huge thank you to all our members for their contributions & making it an engaging event. The day was a resounding success, & we're already looking forward to the next one!
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Applications are now open to join our fully-funded PhD Studentship on "Deep Learning on Neurophysiological Signals for Characterising Neurodegenerative Diseases" Start Date: September 2023 Click here to Apply ➡️ https://t.co/fMwvSn0yj7
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September is #WorldAlzheimersMonth & we'd like to congratulate our researchers @FeiHeUK & @DominikKlepl on their inspiring energy landscape concept which has uncovered a new approach to improve future diagnosis of Alzheimer’s Disease. 👏 @covcampus
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CSM Researchers have share their findings on #Alzheimers. @FeiHeUK & @DominikKlepl attended the 44th @IEEEembs Conference at Glasgow in July. Dominik presented his paper “Bispectrum-based Cross-frequency Functional Connectivity: Classification of #AlzheimerDisease”
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Our paper on EEG-based GNN for AD classification is out in IEEE TNSRE @TNSRE1 - it shows the powerfulness of GNN; we evaluated different functional connectivity measures & compared with ML/DL baseline models. @DominikKlepl
https://t.co/YD5LEbpBd1
#EEG #GNN #Alzheimer
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What functional connectivity measure should you use for constructing brain networks when training Graph Neural Networks? We address this question in our new preprint. https://t.co/5MjMqru9Kx
#EEG #neuroscience #NeuralNetworks
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Our new paper about energy landscapes of Alzheimer's disease patients reconstructed from EEG got selected for the cover of the current issue of IEEE Journal of Biomedical and Health Informatics. https://t.co/TLSHds9zlo
@FeiHeUK
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Our EEG Energy Landscape & AD paper is now published at IEEE JBHI, @DominikKlepl
https://t.co/zOxjNCrf9Y Latest version of preprint: https://t.co/KQxrLcvs2F code:
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We have a new preprint out looking at cross-frequency interactions (measured with bispectrum) in Alzheimer's disease from network perspective
New preprint @DominikKlepl
https://t.co/PAp6NXWVe5 We use cross-bispectrum as a nonlinear FC to construct a multilayer cross-frequency network. With graph-theoretic analysis, it provides a powerful framework to represent/analyse CFC brain network. #Alzheimers #EEG #network
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“The specialist knows more and more about less and less and finally knows everything about nothing” (Konrad Lorenz) Then the multidisciplinary knows less and less about more and more and finally knows nothing about everything?
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In a new @NatureComms paper incoming SFI Fellow @YuanzhaoZhang & former SFI External Prof @stevenstrogatz show that allowing connection #patterns to change over #time makes it possible to #synchronize a #system more efficiently: https://t.co/shiNMCInFs
#synchrony #synchronization
santafe.edu
In a paper published in Nature Communications, incoming SFI Postdoctoral Fellow Yuanzhao Zhang and former SFI external faculty member Steve Strogatz report using temporal network models to show that...
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Massively important paper both reassures fMRI as a tool to study (slow) brain networks, and finds that the (metabolic) body anticipates the brain's needs. https://t.co/oh3GXKgDQU
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New preprint ✨: with @DrVeronikaCH "How I failed machine learning in medical imaging - shortcomings and recommendations" https://t.co/pM13YLxSVI We inspect what are the current roadblocks to the clinical impacts of machine learning research. ⬇️ 1/6
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Giving you a glimpse into the future of stats typesetting: 🎩 β = (X’X)⁻¹X’y
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So proud of how far BioRender has come thanks to our incredible team. We launched 3 years ago w/ just a handful of icons and today we save scientists >1 million hrs every month (time otherwise spent in PPT) 🙏🏽 I hope this helps you in your next project! 💙 https://t.co/apVHwhGUAU
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Curious about GPT-3, the remarkable AI that can write like humans? Come and check it out: https://t.co/WMxC40bO8v
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