Prof. Dr.-med. Lead of project B3 Prof. Dr.-med. Lead of project B3
Role within the Collaborative Research Centre
The proposed project B3 focuses on studying the quality of the recorded signals, provides feedback for the other projects concerning artifacts, signal-to-noise ratio, sensor behavior and signal stability, and validates the ME sensors by first applications to clinical (long-term) recordings. Close cooperation is planned with the following partners:
A8: The influence of ME sensor behavior and crosstalk characteristics of sensor arrays on inverse solutions as well as the effect of non-ideal, i.e. extended sensors with fabrication variations, on results of source reconstruction will be studied.
B2: The analyses of SNR (and its influence on inverse solution) and changes of sensor properties with time, and changing influence of external and internal artifacts during long-term recordings enable feedback concerning quality of analog and digital signal processing.
B5: MEG-EEG data recorded in patients with implanted deep brain stimulator by using variable ME sensor arrays will be provided by
B5. For B5, the project B3 will investigate significance of the number, placement and orientation of ME sensors, perform source reconstruction using static and dynamic methods, and apply algorithms of fusion of simultaneously recorded MEG and EEG data.
B6: Inverse solutions will be provided to characterize activity in the nerve by using ME sensors in the second funding period. The project B3 will be involved in experiments to learn the behavior of ME signals from nerves.
B7: Magnetic particle mapping in the project
B7 will use solutions for the inverse problem developed in B3.
Z2: Construction and further development of heart and head phantoms (more coils for multiple source activity), construction of ME sensor systems and arrays as well as ECG/EEG-ME sensor arrays, performing measurements with scanner).
Project B3 will participate in the focus groups F1 “Modeling” and F3 “Biomagnetic Signal Analysis”. Project-related Publications
L. Hamid, N. Habboush, P. Stern, N. Japaridze, Ü. Aydin, C. H. Wolters, J. C. Claussen, U. Heute, U. Stephani, A. Galka, M. Siniatchkin, Source imaging of deep-brain activity using the regional spatiotemporal Kalman filter, Comput Methods Programs Biomed, 105830 (2020),
A. Galka, S. Monntaha, M. Siniatchkin, Constrained Expectation Maximisation Algorithm for Estimating ARMA models in State Space Representation, EURASIP Journal on Advances in Signal Processing, Springer Nature, accepted (March 2020).
L. Hamid, A. Dalaf, I. Merlet, N. Japaridze, U. Heute, U. Stephani, A Galka, F. Wendling, M. Siniatchkin, Source reconstruction via the spatiotemporal Kalman filter and LORETA from EEG time series with 32 or fewer electrodes.
Engineering in Medicine and Biology Society (EMBC), 2017 39 (2017). DOI: th Annual International Conference of the IEEE , Seogwipo, South Korea, pp. 2218-2222 10.1109/EMBC.2017.8037295
L. Hamid, A. Dalaf, I. Merlet, N. Japaridze, U. Heute, U. Stephani, A. Galka, F. Wendling, M. Siniatchkin, Spatial projection as a preprocessing step for EEG source reconstruction using spatiotemporal Kalman filtering.
Engineering in Medicine and Biology Society (EMBC), 2017 39(2017). DOI: th Annual International Conference of the IEEE , Seogwipo, South Korea, pp. 2213-2217 10.1109/EMBC.2017.8037294
A.R. Anwar, M. Muthalib, S. Perrey, A. Galka, O. Granert, S. Wolff, U. Heute, G. Deuschl, J. Raethjen, Muthuraman M, Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks.
A Simultaneous fNIRS, fMRI, EEG Study, Brain Topogr. (2016). http://dx.doi.org/10.1007/s10548-016-0507-1