Pioneering EEG Decoding

Our proprietary deep learning technology unlocks reliable brainwave decoding across different people and devices without calibration. Unlike traditional approaches requiring lengthy setup procedures, our AI-powered decoders work immediately. Our breakthrough approach, validated through winning the prestigious NeurIPS BEETL Competition, sets a new standard in brain-computer interaction.

Device-Agnostic Innovation

Our software seamlessly integrates with any EEG system that provides suitable coverage, from research-grade to consumer devices. This flexibility enables broad accessibility while maintaining professional-grade performance. The result? Consistent brainwave decoding without being tied to specific hardware.

The new era of wearable devices

Our plug-and-play software allows scalability in the new era of wearable devices, combining world-leading machine learning with world-leading neuro-informatics. We are taking EEG decoding out of the lab and bringing it into people’s homes.

Applications That Matter

Our technology creates meaningful impact across multiple domains - from enabling inclusive Virtual Reality and rehabilitation through neurofeedback, to enhancing professional training with attention monitoring and supporting personal wellbeing through meditation guidance. By bridging mind and machine, we're opening new possibilities that matter in healthcare, workplace, and everyday life.

Patents

Our innovative solutions are protected by the following patents

Learnable Filters for EEG Classification

GR Patent Office: GR20210100506
GB Intellectual Property Office: GB2609265
International Patent (PCT): WO/2023/007118
US Patent Office: US 20240366140 A1
European Patent Office (EPO): EP4346604

Classification of Brain Activity Signals

GR Patent Office: GR20220100122
GB Intellectual Property Office: GB2605270
International Patent (PCT): WO/2023/148471
US Patent Office: US 18/835,771 pending
European Patent Office (EPO): EP4452055

Publications

Cogitat produces cutting edge research in Machine Learning and Brain-Computer Interfaces, including the following recent publications

Bakas, S., Ludwig, S., Adamos, D.A., Laskaris, N., Panagakis, Y. and Zafeiriou, S. (2025) Latent alignment in deep learning models for EEG decoding. Jounal of Neural Engineering
Barmpas, K., Zoumpourlis, G., Panagakis, Y., Adamos, D., Laskaris, N. and Zafeiriou, S. (2024) Position: Addressing Ethical Challenges and Safety Risks in GenAI-Powered Brain-Computer Interfaces. In NeurIPS 2024 Workshop on GenAI for Health: Potential, Trust and Policy Compliance.
Barmpas, K., Panagakis, Y., Adamos, D., Laskaris, N. and Zafeiriou, S. (2024) A Causal Perspective in Brainwave Foundation Models. In NeurIPS2024 Workshop on Causality and Large Models.
Ludwig, S., Bakas, S., Adamos, D.A., Laskaris, N., Panagakis, Y. and Zafeiriou, S. (2024) EEGminer: discovering interpretable features of brain activity with learnable filters. Journal of Neural Engineering, 21(3), p.036010.
Barmpas, K., Panagakis, Y., Zoumpourlis, G., Adamos, D.A., Laskaris, N. and Zafeiriou, S. (2024) A Causal Perspective on Brainwave Modeling for Brain-Computer Interfaces. Journal of Neural Engineering, 21(3), p.036001.
Barmpas, K., Panagakis, Y., Adamos, D.A., Laskaris, N. and Zafeiriou, S. (2024) Subject Selection Framework to Improve Personalised Models for Motor-Imagery BCIs via Wavelets and Graph Diffusion. In ICLR 2024 In ICLR2024 Workshop on Learning for Time Series for Health.
Barmpas, K., Panagakis, Y., Adamos, D.A., Laskaris, N. and Zafeiriou, S. (2023) BrainWave-Scattering Net: a lightweight network for EEG-based motor imagery recognition. Journal of Neural Engineering, 20(5), p.056014.
Barmpas, K., Panagakis, Y., Bakas, S., Adamos, D.A., Laskaris, N. and Zafeiriou, S. (2023) Improving Generalization of CNN-Based Motor-Imagery EEG Decoders via Dynamic Convolutions. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, pp.1997-2005.
Wei, X., Faisal, A.A., Grosse-Wentrup, M., Gramfort, A., Chevallier, S., Jayaram, V., Jeunet, C., Bakas, S., Ludwig, S., Barmpas, K. and Bahri, M. (2022) 2021 BEETL Competition: Advancing Transfer Learning for Subject Independence and Heterogenous EEG Data Sets. In NeurIPS 2021 Competitions and Demonstrations Track (pp. 205-219). PMLR.
Barmpas, K., Panagakis, Y., Adamos, D., Laskaris, N. and Zafeiriou, S. (2022) A Causal Viewpoint on Motor-Imagery Brainwave Decoding. In ICLR2022 Workshop on the Elements of Reasoning: Objects, Structure and Causality.

Our Technology