Jun.-Prof. Dr.-Ing. Patrique Fiedler
patrique.fiedler@tu-ilmenau.de
Telefon: 03677/ 69 28 65
AG Datenanalyse in den Lebenswissenschaften
>>Forschungsschwerpunkte
Within ThiMEDOP, the research group “Data Analysis in Life Sciences" focuses on the areas of:
- Sensor technologies for biosignals (especially bioelectric signals including EEG, ECG, EMG)
- Methods for automatic artefact detection and correction in biosignals
- Multimodal data acquisition and data fusion
- Close-to-hardware and close-to-sensor biosignal processing
- Machine Learning for biomedical applications
Our group is interested in supporting R&D on novel mobile and intelligent sensor technologies and signal processing for monitoring electrophysiological, respiratory and hemodynamic activity in medical applications. Within the overall frame of R&D on sensor technologies, signal processing, and analysis for mobile medical monitoring and body sensor network applications, we collaborate with partners in industry and academia and cover three main areas of expertise:
- Integration of novel materials and composites into comprehensive electronic sensor components for sequential or simultaneous multimodal biosignal acquisition.
- Development and integration of mechanical components for sensor placement and fixation on the human body for optimal data quality and applicability in mobile use cases.
- Development and integration of biosignal processing methods for signal enhancement, artefact detection / correction, and machine learning approaches for automated analysis and classification.
Publikationen
2024
Tamburro, G., Bruna, R., Fiedler, P., De Fano, Antonio, Raeisi, K., Khazaei, M., Zappasodi, F., Comani, S.: An Analytical Approach for Naturalistic Cooperative and Competitive EEG-Hyperscanning Data: A Proof-of-Concept Study, Sensors, 2024, vol. 24, 2995 https://doi.org/10.3390/s24102995
2023
Fiedler, P., Graichen, U.; Zimmer, E.; Haueisen, J.: Simultaneous dry and gel-based high-density EEG recordings, Sensors, 2023, vol. 23, 9745 https://doi.org/10.3390/s23249745
Tamburro, G.*; Fiedler, P.*; De Fano, A.; Raeisi Nafchi, K.; Khazaei, M.; Bruña, R.; Filho, E.; Zappasodi, F.; Comani, S.: An ecological study protocol for the multimodal investigation of the neurophysiological underpinnings of dyadic joint action, Frontiers in Human Neuroscience, 2023, vol. 17, 1305331 https://doi.org/10.3389/fnhum.2023.1305331
Warsito, I.F.; Komosar, M.; Bernhard, M.A.; Fiedler, P.; Haueisen, J.: Flower electrodes for comfortable dry electroencephalography; Scientific Reports, 2023, Vol. 13, 16589 http://dx.doi.org/10.1038/s41598-023-42732-8
Pusil, S.; Zegarra-Valdivia, J.; Cuesta, P.; Laohathai, C.; Cebolla, A.M.; Haueisen, J.; Fiedler, P.; Funke, M.; Maestú, F.; Cheron, G.: Effects of spaceflight on the default mode network: EEG insights in brain alpha power and functional connectivity; Scientific Reports, 2023, Vol. 13, 9489 https://doi.org/10.1038/s41598-023-34744-1
Fiedler, P.; Haueisen, J.; Cebolla Alvarez A.M.; Cheron, G.; Cuesta, P.; Maestú, F.; Funke, M.: Noise characteristics in spaceflight multichannel EEG; PLoS One, 2023, Vol. 18, No. 2, e0280822 https://doi.org/10.1371/journal.pone.0280822
2022
Ng, C.R.; Fiedler, P.; Kuhlmann, L.; Liley, D.; Vasconcelos, B.; Fonseca, C.; Tamburro, G.; Comani, S.; Lui, T. K.-Y.; Tse, C.-Y.; Warsito, I.F.; Supriyanto, E.; Haueisen, J.: Multi-center evaluation of gel-based and dry Multipin EEG caps, Sensors, 2022, Vol. 22, No. 20, 8079 https://doi.org/10.3390/s22208079
Croce, P.; Franca, T.; Tamburro, G.; Fiedler, P.; Comani, S.; Zappasodi, F.: Brain Electric Microstates for the Control of the Steady State Motor Output; Journal of Neural Engineering, 2022, Vol. 19, 056042 https://doi.org/10.1088/1741-2552/ac975b
Fiedler, P.; Fonseca, C.; Supriyanto, E.; Zanow, F.; Haueisen, J.: A high-density 256-channel cap for dry electroencephalography; Human Brain Mapping, 2022, Vol. 43, No. 4, pp. 1295-1308 https://doi.org/10.1002/hbm.25721
2021
Lopes, C.; Fiedler, P.; Alves, E.; Barradas, N.P.; Vaz, F.: Me doped Ti-Me intermetallic thin films used for dry biopotential electrodes: A comparative case study; Sensors, 2021, Vol. 21, No. 23, 8143 https://doi.org/10.3390/s21238143
Vasconcelos, B.*; Fiedler, P.*; Machts, R.; Haueisen, J.; Fonseca, C.: The Arch Electrode: A Novel Dry Electrode Concept for Improved Wearing Comfort; Frontiers in Neurosciences, 2021, Vol. 15, 748100 *First two authors acknowledge equal authorship https://doi.org/10.3389/fnins.2021.748100
Heijs, J.J.A.; Havelaar, R.J.; Fiedler, P.; Van Wezel, R.J.A.; Heida, T.: Validation of soft, multipin, dry EEG electrodes; Sensors, 2021, Vol. 21, 6827 https://doi.org/10.3390/s21206827
Mulyadi, I.H.; Fiedler, P.; Eichardt, R.; Haueisen, J.; Supriyanto, E.: Pareto Optimization for Electrodes Placement on ECG Smart Shirts: Compromises between Electrophysiological and Practical Aspects; Medical & Biological Engineering & Computing, 2021, Vol. 59, No. 2, pp. 431-447
2020
Rodrigues, M.S.; Fiedler, P.; Küchler, N.; Domingues, R.P. ; Lopes, C. ; Borges, J.; Haueisen, J.; Vaz, F.: Dry Electrodes for Surface Electromyography Based on Architectured Titanium Thin Films; Materials, 2020, Vol. 13, 2135
2019
di Fronso, S.; Fiedler, P.; Tamburro, G.; Haueisen, J.; Bertollo, M.; Comani, S.: Dry EEG in Sports Sciences: A Fast and Reliable Tool to Assess Individual Alpha Peak Frequency Changes Induced by Physical Effort; Frontiers in Neuroscience, 2019, Vol. 13:982 *First two authors acknowledge equal authorship
2018
Piątek, Ł.; Fiedler, P.; Haueisen, J.: Eye State Classification from EEG Recordings using Machine Learning Algorithms; Digital Medicine, 2018, Vol. 4, No. 2, pp. 84-95
Virtanen, J.; Somppi, S.; Törnqvist, H.; Jeyhani, V.; Fiedler, P.; Gizatdinova, Y.; Majaranta, P.; Väätäjä, H.; Cardó A.V.; Lekkala, J.; Tuukkanen, S.; Surakka, V.; Vainio, O.; Vehkaoja, A.: Evaluation of Dry Electrodes in Canine Heart Rate Monitoring; Sensors, 2018, Vol., 18, No. 6
Wunder, S.; Hunold, A.; Fiedler, P.; Schlegelmilch, F.; Schellhorn, K.; Haueisen, J.: Novel bifunctional cap for simultaneous electroencephalography and transcranial electrical stimulation; Nature Scientific Reports, 2018, Vol. 8, 7259
Stone, D.; Tamburro, G.; Fiedler, P.; Haueisen, J.; Comani, S.: Automatic removal of physiological artifacts in EEG: the Optimized Fingerprint Method for sports science applications; Frontiers in Human Neuroscience, 2018, Vol. 12:96, No. 96
Tamburro, G.; Fiedler, P.; Stone, D.; Haueisen, J.; Comani, S.: A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings; PeerJ, 2018, Peer J, 2018, Vol. 6, e4380
Fiedler, P.; Mühle, R.; Griebel, S.; Pedrosa, P.; Fonseca, C.; Vaz, F.; Zanow, F.; Haueisen, J.: Contact Pressure and Flexibility of Multipin Dry EEG Electrodes; IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, Vol. 26, No. 4, pp. 750-757