Department of Health Technologies

Head of the research group:  Professor MAIE BACHMANN,
Members: Hiie Hinrikus, Jaanus Lass,  Laura Päeske
Doctoral students: Toomas Põld, Tuuli Uudeberg

Topics and competences
Keywords: signal processing, electroencephalography, brain disorders

The research group is experienced in the interdisciplinary area of information technology and brain physiology. The studies are aimed to detect and interpret the features in the brain electroencephalography (EEG) signal characteristic for mental disorder, occupational and/or environmental stressors comprising the advanced methods of signal analysis and the knowledge about brain neuronal activity. An original Spectral Asymmetry Index (SASI) has been developed and proved as a promising method in various applications.

Recent results:

• By applying EEG-based objective measures it is feasible to indicate early symptoms of depression;
• Long-term temporal variability of SASI for a person is much lower than inter-individual variability, supporting the possibility of using SASI for evaluation of depression symptoms for an individual;
• Higher SASI values for the subgroup of leaders compared to non-leaders could indicate higher psychological stress of leaders.

Selected projects:

• TAR16013 (EXCITE) (TK148) "Estonian Centre of Excellence in ICT Research" (2016−2023);
• 5GEMF1 "Assessment of current and 5G caused possible health effects related to nonionizing radiation" (2021−2022)
• COST Action CA18106 "The neural architecture of consciousness" (2019-2023)
• COST Action CA19127 "Cognitive decline in Nephro-Neurology: European Cooperative Target" (2020-2024)

Selected articles

• Hinrikus, H.; Lass, J.; Bachmann, M. (2021) Threshold of radiofrequency electromagnetic field effect on human brain. Int J Radiat Biol, DOI: 10.1080/09553002.2021.1969055. Online ahead of print.
• Põld, T.; Päeske, L.; Hinrikus, H.; Lass, J.; Bachmann, M. (2021). Long-term stability of resting state EEG-based linear and nonlinear measures. International Journal of Psychophysiology, 159, 83−87. DOI: 10.1016/j.ijpsycho.2020.11.013.
• Päeske, L.; Hinrikus, H.; Lass, J.; Raik, J.; Bachmann, M. (2020). Negative Correlation Between Functional Connectivity and Small-Worldness in the Alpha Frequency Band of a Healthy Brain. Frontiers in Physiology, 11 (910). DOI: 10.3389/fphys.2020.00910. 
• Põld, T.; Päske, L.; Bachmann, M.; Lass, J.; Hinrikus, H. (2019). Assessment of Objective Symptoms of Depression in Occupational Health Examination. Journal of Occupational and Environmental Medicine, 61 (7), 605−609.
• Bachmann, M.; Päeske, L.; Kalev, K.; Aarma, K.; Lehtmets, A.; Ööpik, P.; Lass, J.; Hinrikus, H. (2018). Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis. Computer Methods and Programs in Biomedicine, 155, 11−17.10.1016/j.cmpb.2017.11.023.
• Päeske, L.; Bachmann, M.; Põld, T.; de Oliveira, S. P. M.; Lass, J.; Raik, J.; Hinrikus, H. (2018). Surrogate Data Method Requires End-Matched Segmentation of Electroencephalographic Signals to Estimate Non-linearity. Frontiers in Physiology, 9, ARTN 1350.10.3389/fphys.2018.01350.
• Bachmann, M.; Päeske, L.; Ioannides, A. A; Lass, J.; Hinrikus, H. (2018). After-effect induced by microwave radiation in human electroencephalographic signal: a feasibility study. International Journal of Radiation Biology, 94 (10), 896−901.10.1080/09553002.2018.1478164.
• Hinrikus, H.; Bachmann, M.; Lass, J. (2018). Understanding physical mechanism of low-level microwave radiation effect. International Journal of Radiation Biology, 94 (10), 877−882.10.1080/09553002.2018.1478158.