Imperial College London


Faculty of EngineeringDepartment of Computing

Honorary Senior Research Fellow



d.adamos Website




304Huxley BuildingSouth Kensington Campus





Dr. Dimitrios Adamos is an Honorary Senior 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.


Selected Publications

Journal Articles

Barmpas K, Panagakis Y, Adamos DA, et al., 2023, BrainWave-Scattering Net: a lightweight network for EEG-based motor imagery recognition, Journal of Neural Engineering, Vol:20, ISSN:1741-2560

Barmpas K, Panagakis Y, Bakas S, et al., 2023, Improving Generalization of CNN-Based Motor-Imagery EEG Decoders via Dynamic Convolutions, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol:31, ISSN:1534-4320, Pages:1997-2005

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

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


Laskaris N, Adamos D, Bezerianos A, 2023, Graph Theory for Brain Signal Processing, Handbook of Neuroengineering, Editor(s): Thakor, Springer Nature, ISBN:9789811655401


Wei X, Aldo Faisal A, Grosse-Wentrup M, et al., 2022, 2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets, Pages:205-219

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