Dr. Dimitrios Adamos is an Honorary Research Fellow for the Department of Computing of Imperial College London and the leader of the #MyBrainTunes experiment held in London’s Science Museum. He is also a co-founder and the CTO of Cogitat, an Imperial College spinout company that develops core AI/ML technology for brain wave decoding.
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 Aristotle University of Thessaloniki, Greece. His research work focuses on machine learning for real-life Brain-Computer Interface applications and has previously featured as invited technology demonstrator for the industry.
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
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
et al., 2020, A complex-valued functional brain connectivity descriptor amenable to Riemannian geometry, Journal of Neural Engineering, Vol:17, ISSN:1741-2560
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
et al., EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters
et al., 2021, A graph-theoretic sensor-selection scheme for covariance-based motor imagery (MI) decoding, Pages:1234-1238, ISSN:2219-5491