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Abstract
Dynamic imaging of source and functional connectivity (FC) using electroencephalographic (EEG) signals is essential for understanding the brain and cognition with sufficiently affordable technology to be widely applicable for studying changes associated with healthy ageing and the progression of neuropathology. We present an application for group analysis of recently developed state-space models and algorithms for simultaneously estimating the large-scale EEG inverse and FC problems. This approach reduces estimation bias and facilitates a detailed exploration and investigation of neuronal dynamics compared to current techniques. We present feasibility analyses for simulated and real EEG event-related data. The latter analysis uses a sixteen subjects EEG (Wakeman and Henson's) database, with signals recorded during a face-processing task. We implement a state-space methodology efficiently using an alternating least squares (ALS) algorithm. This application to neuroimaging analysis may be critical to reliably capture the brain dynamics despite interindividual variability, as demonstrated by the results presented.
Original language | English |
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Title of host publication | ICASSPW 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops, Proceedings |
Place of Publication | U. S. A. |
Publisher | IEEE |
Pages | 1-5 |
Number of pages | 5 |
ISBN (Electronic) | 9798350302615 |
DOIs | |
Publication status | Published - 2 Aug 2023 |
Event | 2023 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops, ICASSPW 2023 - Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023 |
Publication series
Name | ICASSPW 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops, Proceedings |
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Conference
Conference | 2023 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops, ICASSPW 2023 |
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Country/Territory | Greece |
City | Rhodes Island |
Period | 4/06/23 → 10/06/23 |
Keywords
- Dynamic functional connectivity
- EEG/MEG event-related source imaging
- Feasibility analysis
- Machine learning
- State-space models
ASJC Scopus subject areas
- Computer Science Applications
- Acoustics and Ultrasonics
- Computer Networks and Communications
- Information Systems
- Signal Processing
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Turing AI Fellowship: AI for Intelligent Neurotechnology and Human-Machine Symbiosis
Coyle, D. (PI), Du Bois, N. (Researcher), Khodadadzadeh, M. (Researcher) & Korik, A. (Researcher)
Engineering and Physical Sciences Research Council
2/02/23 → 31/12/25
Project: Research council