Tarmo Lipping

• Professor at the Faculty of Information Technology and Communication Sciences in the Computing Science Unit, head of the Data Analytics and Optimization research group in Pori​
• Research focus: Development of machine learning and AI-assisted tools to support physical and mental well-being​
• Expertise in: Biomedical signal analysis, machine learning, multimodal data integration, AI model development ​
• Collaboration offering: Physiological data acquisition using wireless sensors for monitoring and application validation, development of machine learning and AI frameworks

Machine learning; 

Biomedical data analysis; 

Signal processing; 

AI model development

Lipping, T, Sharif, A, Beiramvand, M & Turunen, J 2025, Detecting Interbrain Synchronization in EEG Hyperscanning with MUSE-S EEG Headband. IEEE Portuguese Meeting on Bioengineering, IEEE, pp. 61-64, IEEE Portuguese Meeting on Bioengineering, Aveiro, Portugal, 25/09/25. https://doi.org/10.1109/ENBENG67130.2025.11199533

Beiram Vand, M, Koivula, R & Lipping, T 2025, Development of an EEG-Based Method for Detecting Flow State Using a Wearable Headband in a Game Environment. in 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/EMBC58623.2025.11251885

Beiramvand, M, Shahbakhti, M, Karttunen, N, Koivula, R, Turunen, J & Lipping, T 2024, 'Assessment of Mental Workload Using a Transformer Network and Two Prefrontal EEG Channels: An Unparameterized Approach', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-10. https://doi.org/10.1109/TIM.2024.3395312

Lipping, T & Beiramvand, M 2024, Assessment of Mental Workload in Real-Life Setup using EEG Synchronization Measures. Proceedings IEEE International Workshop on Metrology for Industry 4.0 and IoT, IEEE, pp. 412-416, Firenze, Italy, 29/05/24. https://doi.org/10.1109/MetroInd4.0IoT61288.2024.10584156

Beiram Vand, M, Shahbakhti, M & Lipping, T 2024, Evaluating Mental Workload Through Cross-Entropy Analysis of Two Prefrontal EEG Channels. IFMBE proceedings, Springer, European Medical and Biological Engineering Conference, Portorož, Slovenia,  9/06/24. https://doi.org/10.1007/978-3-031-61628-0_5

Shahbakhti, M, Krycinska, R, Beiramvand, M, Hakimi, N, Lipping, T, Chen, W, Broniec-Wojcik, A, Augustyniak, P, Tanaka, T, Sole-Casals, J, Wierzchon, M & Wordliczek, J 2024, 'Wearable EEG-Based Depth of Anesthesia Monitoring: A Non-Parametric Feature Set', IEEE Sensors Journal, vol. 24, no. 11. https://doi.org/10.1109/JSEN.2024.3390604

Beiramvand, M, Lipping, T, Karttunen, N & Koivula, R 2023, Mental Workload Assessment using Low-Channel Prefrontal EEG Signals. IEEE International Symposium on Medical Measurements and Applications, Jeju, Korea, Republic of, 14/06/23. https://doi.org/10.1109/memea57477.2023.10171942

Beiram Vand, M, Shahbakhti, M & Lipping, T 2023, Cross-Entropy-Based Assessment of Mental Workloads Using Two Prefrontal EEG Channels. in 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology. IEEE, pp. 49-50, St. Julians, Malta, 7/12/23. https://doi.org/10.1109/IEEECONF58974.2023.10404709

Zabihi, M, Kiranyaz, S, Jäntti, V, Lipping, T & Gabbouj, M 2020, 'Patient-Specific Seizure Detection Using Nonlinear Dynamics and Nullclines', IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 2, pp. 543-555. https://doi.org/10.1109/JBHI.2019.2906400

Kuhlmann, L, Freestone, D, Manton, J, Heyse, B, Vereecke, H, Lipping, T, Struys, M & Liley, D 2016, 'Neural mass model-based tracking of anesthetic brain states', NeuroImage, vol. 133, pp. 438-456. https://doi.org/10.1016/j.neuroimage.2016.03.039