To measure signals of the heart or the brain with a high temporal resolution, usually electrical based measurement types as Electrocardiography (ECG) resp. Electroencephalography (EEG) or magnetic based measurement types as Magnetocardiography (MCG) resp. Magnetoencephalography (MEG) are used. Since both types of measurements have their advantages and disadvantages both methods should be used for clinical diagnosis. Unfortunately, the operation of suitable super-conducting quantum interference devices (SQUIDs) for magnetic based measurements is in general very expensive.
In order to provide an alternative, sensors have been developed based on the ME-effect in recent years in the collaborative research groups at Kiel University. These sensors have the potential to be an appropriate alternative to SQUIDs. Unfortunately, these sensors also record mechanical vibrations, whereby the desired signals are often superimposed by unwanted signal components. To reduce this effect, an adaptive cancellation approach using non-magnetic noise reference sensors is realized by us. This approach is realized in real-time using our own tool (for details see here).
These sensors are utilized for magnetoneurography analysis. This approach provides an alternative and also very attractive technology to the electrode-based nerve conduction studies called Electroneurography. The main objective of this modern technique is to optimize the diagnostic specificity of neuropathies by developing a spatially continuous scanning of nerves with a magnetic field sensor. This approach will increase and also combine the spatial resolution and functional information. In summary, this will further improve the treatment.
Furthermore, we work on brain-computer interfaces (right now based on pure electrical interfaces, but hopefully soon also using magneto-electric sensors). On the picture on the right you see one of our brain-computer interfaces that is used for controlling a TV set (beside the brain modality also speech can be used for controlling the device).
Finally we also research on the analysis of Parkinson speech. Morbus Parkinson is a wide spread disease especially in the age group above 50 years. Besides several motor disorders also speech disorders accompany this disease. The dysarthria expresses itself, among other symptoms, by a too low speech level and a mumbling voice. Probably due to a perception disorder, the patients do not realize their dysfunction in speech production. The investigation of the efficacy of these therapies has been and is still a very interesting research topic. In contrast, not yet established is the investigation of the origin and location of the problem in speech production. This would help to adapt the speech therapy to the specific needs of the individual patient. This can be realized by an evaluation of the patient’s speech using technical measures. Further, these measures can be used instead of auditive assessments to classify the severity of the patients dysarthria. The goal is the development of a tool that is capable to perform such analyses. It includes the recording equipment that is needed to make reliable recordings of the patients, as well as a recording environment, implemented in a real-time signal processing toolkit, measures to evaluate the speech disorder, and, ﬁnally, the verification of the selected measures.
Details about the projects can be found on the links below:
J. Reermann, P. Durdaut, S. Salzer, T. Demming,A. Piorra, E. Quandt, N. Frey, M. Höft, and G. Schmidt: Evaluation of Magnetoelectric Sensor Systems for Cardiological Applications, Measurement (Elsevier), ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2017.09.047, 2017
M. H. Pham, M. Elshehabi, L. Haertner, S. D. Din, K. Srulijes, T. Heger, M. Synofzik, M. A. Hobert, G. S. Faber, C. Hansen, D. Salkovic, J. J. Ferreira, D. Berg, A. Sanchez-Ferro, J. H. van Dieën, C. Becker, L. Rochester, G. Schmidt, and W. Maetzler: Validation of a Step Detection Algorithm during Straight Walking and Turning in Patients with Parkinson’s Disease and Older Adults Using an Inertial Measurement Unit at the Lower Back, Front. Neurol. 8:457, 2017 (doi: 10.3389/fneur.2017.00457)
P. Durdaut, J. Reermann, S. Zabel, Ch. Kirchhof, E. Quandt, F. Faupel, G. Schmidt, R. Knöchel, and M. Höft: Modeling and Analysis of Noise Sources for Thin-Film Magnetoelectric Sensors Based on the Delta-E Effect, IEEE Transactions on Instrumentation and Measurement, published online, 2017
P. Durdaut, S. Salzer, J. Reermann, V. Röbisch, J. McCord, D. Meyners, E. Quandt, G. Schmidt, R. Knöchel, and M. Höft: Improved Magnetic Frequency Conversion Approach for Magnetoelectric Sensors, IEEE Sensors Letters, published online, 2017
C. Baasch, G. Schmidt, U. Heute, A. Nebel, and G. Deuschl: Parkinson-Sprachanalyse - Erweiterungen zum Qualitätsmerkmal Formantdreieck, Proc. DAGA, Kiel, Germany, open access, 2017
J. Reermann, C. Bald, P. Durdaut, A.Piorra, D. Meyners, E. Quandt, M. Höft, and G. Schmidt: Adaptive mehrkanalige Geräuschkompensation für magnetoelektrische Sensoren, Proc. DAGA, Kiel, Germany, open access, 2017
P. Durdaut, S. Salzer, J. Reermann, V. Röbisch, P. Hayes, A. Piorra, D. Meyners, E. Quandt, G. Schmidt, R. Knöchel, M. Höft: Thermal-Mechanical Noise in Resonant Thin-Film Magnetoelectric Sensors, IEEE Sensors Journal, published online, 2017
V. Röbisch, S. Salzer, N. O. Urs, J. Reermann, E. Yara, A. Piorra, C. Kirchhof, E. Lage, M. Höft, G. Schmidt, R. Knöchel, J. McCord, E. Quandt, and D. Meyners: Pushing the Detection Limit of Thin Film Magnetoelectric Heterostructures, Journal of Materials Research, published online, 2017
J. Reermann, C. Bald, S. Salzer, P. Durdaut, A. Piorra, D. Meyners, E. Quandt, M. Höft, and Gerhard Schmidt: Comparison of Reference Sensors for Noise Cancellation of Magnetoelectric Sensors, IEEE Sensors 2016, Orlando, November 2016
C. Baasch, G. Schmidt, U. Heute, A. Nebel, G. Deuschl: Parkinson-Speech Analysis: Methods and Aims, ITG Speech, October 2016
P. Caldero-Bardaji, X. Longfei, S. Jaschke, J. Reermann, K.G. Mideska, G. Schmidt, G. Deuschl, M. Muthuraman: Detection of steering direction using EEG recordings based on sample entropy and time-frequency analysis, EMBC 2016, Orlando, August 2016
J. Reermann, S. Zabel, Ch. Kirchhof, E. Quandt, F. Faupel, G. Schmidt: Adaptive Readout Schemes for Thin-Film Magnetoelectric Sensors Based on the delta-E Effect, IEEE Sensors Journal, Volume 16, Number 12, Pages 4891-4900, June 2016
S. Zabel, J. Reermann, S. Fichtner, C. Kirchhof, E. Quandt, B. Wagner, G. Schmidt, and F. Faupel: Multimode Delta-E Effect Magnetic Field Sensors with Adapted Electrodes, Applied Physics Letter, Volume 108, Number 22, 2016
C. Baasch, W. Schmidt, G. Schmidt, U. Heute, A. Baumann, A. Nebel, G. Deuschl, T. von Eimeren: Stimmtherapie für Parkinsonsprache: Akustische Analyse der Wirksamkeit, ESSV 2016, Leipzig, Germany
P. Hayes, S. Salzer, J. Reermann, E. Yarar, V. Röbisch, A. Piorra, D. Meyners, M. Höft, R. Knöchel, G. Schmidt, E. Quandt: Electrically Modulated Magnetoelectric Sensors, Applied Physics Letters, Volume 108, Number 18, 2016
S. Salzer, R. Jahns, A. Piorra, I. Teliban, J. Reermann, M. Höft, E. Quandt, R. Knöchel: Tuning Fork for Noise Suppression in Magnetoelectric Sensors, Sensors and Actuators A: Physical, Volume 237, Pages 91-95, January 2016
J. Reermann, G. Schmidt, I. Teliban, S. Salzer, M. Höft, R. Knöchel, A. Piorra, E. Quandt: Adaptive Acoustic Noise Cancellation for Magnetoelectric Sensors, IEEE Sensors Journal, Volume 15, Number 10, Pages 5804-5812, October 2015
J. Reermann, G. Schmidt, S. Zabel, F. Faupel: Adaptive Multi-mode Combination for Magnetoelectric Sensors Based on the delta-E Effect, Procedia Engineering, Eurosensors 2015, Volume 120, Pages 536-539, September 2015
K. G. Mideksa, A. Santillan-Guzman, N. Japaridze, A. Galka, U. Stephani, G. Deuschl, U. Heute, M. Muthuraman: Validating the Effect of Muscle Artifact Suppression in Localizing Focal Epilepsy, Proc. EMBC 2014, IEEE-EMBS, Chicago, USA, 2014
K. G. Mideksa, N. Hoogenboom, H. Hellriegel, H. Krause, A. Schnitzler, G. Deuschl, J. Raethjen, U. Heute, M. Muthuraman: Impact of Head Modeling and Sensor Types in Localizing Human Gamma-band Oscillations , Proc. EMBC 2014, IEEE-EMBS, Chicago, USA, 2014
U. Heute, A. Santillán Guzmán: Removing "Cleaned" Eye-blinking Artifacts from EEG Measurement, Proc. SPIN '14, 2014, Delhi, India
F. Hinterleitner, C. R. Norrenbrock, S. Möller, U. Heute: Predicting the Quality of Text-To-Speech Systems from a Large-Scale Feature Set, Proc. Interspeech, Lyon, France, 2013
K. G. Mideksa, A. Khan, G. Deuschl, U. Heute, M. Muthuraman: Dipole Source Analysis for Identifying the Location of Deep Brain Stimulation Electrodes in Parkinson's Patients, Proc. EMBC 2013, JSMBE-EMBS, Osaka, Japan, 2013
K. G. Mideksa,H. Hellriegel, N. Hoogenboom, H. Krause, A. Schnitzler, G. Deuschl, J. Raethjen, U. Heute, M. Muthuraman: Dipole Source Analysis for Readiness Potential and Field using Simultaneously Measured EEG and MEG Signals, Proc. EMBC 2013, IEEE-EMBS, Osaka, Japan, 2013
A. Santillán Guzmán, U. Heute, M. Muthuraman, U. Stephani, and A. Galka: DBS Artifact Suppression using a Time-frequency Domain Filter, Proc. EMBC 2013, IEEE-EMBS, Osaka, Japan, 2013
A. Santillán Guzmán, M. Fischer, U. Heute, and G. Schmidt: Real-time Empirical Mode Decomposition for EEG Signal Enhancement, Proc. EUSIPCO 2013, Marrakesh, Morocco, 2013
A. Santillán Guzmán, U. Heute, U. Stephani, H. Muhle, and A. Galka: Hybrid Filter for Removing Power-supply Artifacts from EEG Signals, Proc. BioMed 2013, IAESTED, Innsbruck, Austria, 2013
A. Santillán Guzmán, A. Galka, U.Heute, U. Stephani: Application of State-Space Modeling to Instantaneous Independent Component Analysis, Proc. IEEE International Conference on Biomedical Engineering and Informatics 2011, Shanghai, China, 2011
K. Seget, A. Schulz, U. Heute: Maneuver-Adaptive Multi-Hypothesis Tracking for Active Sonar Systems, Proc. IEEE ISIF GI Workshop Sensor Data Fusion 2010, Leipzig, Germany, 2010