Summary
Dr. Dimitrios A.Adamos is an Honorary Research Fellow for the Department of Computing of Imperial College London and leader of the #MyBrainTunes experiment held in London’s Science Museum. He is also a Senior Research fellow at the School of Music Studies, Aristotle University of Thessaloniki (AUTh), Greece. He holds a MEng in Electrical & Computer Engineering, an MSc in Medical informatics from the School of Medicine and a PhD in Neuroinformatics from the School of Biology of AUTh. His current research includes deep learning and brain connectomics.
Selected Publications
Journal Articles
Bakas S, Adamos DA, Laskaris N, 2021, On the estimate of music appraisal from surface EEG: a dynamic-network approach based on cross-sensor PAC measurements, Journal of Neural Engineering, Vol:18, ISSN:1741-2560
Georgiadis K, Adamos DA, Nikolopoulos S, et al., 2021, Covariation Informed Graph Slepians for Motor Imagery Decoding, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol:29, ISSN:1534-4320, Pages:340-349
Kalaganis FP, Laskaris NA, Chatzilari E, et al., 2020, A complex-valued functional brain connectivity descriptor amenable to Riemannian geometry, Journal of Neural Engineering, Vol:17, ISSN:1741-2560
Kosti MV, Georgiadis K, Adamos DA, et al., 2018, Towards an affordable brain computer interface for the assessment of programmers' mental workload, International Journal of Human-Computer Studies, Vol:115, ISSN:1071-5819, Pages:52-66
Adamos DA, Laskaris NA, Micheloyannis S, 2018, Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening, Journal of Neural Engineering, Vol:15, ISSN:1741-2560
Kalaganis FP, Adamos DA, Laskaris NA, 2018, Musical NeuroPicks: A consumer-grade BCI for on-demand music streaming services, Neurocomputing, Vol:280, ISSN:0925-2312, Pages:65-75
Adamos DA, Dimitriadis SI, Laskaris NA, 2016, Towards the bio-personalization of music recommendation systems: A single-sensor EEG biomarker of subjective music preference, Information Sciences, Vol:343, ISSN:0020-0255, Pages:94-108
Ludwig S, Bakas S, Adamos DA, et al., EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters
Conference
Georgiadis K, Adamos DA, Nikolopoulos S, et al., 2021, A graph-theoretic sensor-selection scheme for covariance-based motor imagery (MI) decoding, Pages:1234-1238, ISSN:2219-5491