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Translational endophenotypes (neuromarkers) in neurodevelopmental disorders: From mouse to man in CLN3 (Batten) disease
Prof. John J. FoxeDone
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Language mapping on patients with parenchymatous tumor in language eloquent areas
Jimmy Landry Zepa YotedjeDone
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Own data, not hardware
Cecilia Mazzetti, PhDDone
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Phase-amplitude coupling in EEG as a Parkinsonian biomarker
Prof. Thomas R. KnöscheDone
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The potential of brain rhythms to gauge the vulnerability of an individual to developing chronic pain
Prof. Ali MazaheriDone
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Towards personalised neuromodulation in mental health: A non-invasive avenue of network research into dynamic brain circuits and their dysfunction
Prof. Marcus KaiserDone
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The Berger’s discovery revisited: How and why the brain’s dominant rhythm relates to cognition
Tzvetan Popov, PhDDone
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Atypical neural processing in 22q11.2 Deletion Syndrome and schizophrenia: Towards neuromarkers of disease progression and risk
Prof. Sophie MolholmDone
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Real world AI in neurosciences for the benefit of doctors and patients
Stephane Doyen, PhDDone
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The condition and perturb approach, a new protocol for preoperative language mapping in patients with brain tumors: First results of intraoperative validation
Tammam Abboud, MDDone
Patrique Fiedler studied electrical engineering and information technology at the Technical University Ilmenau. He received his PhD in biomedical engineering in 2017. He then moved to industry from 2017 to 2021 and held various development, project and product management positions at an internationally active medical technology manufacturer. Mr. Fiedler has been a visiting scientist at the University of Porto in Portugal and the University of Pescara-Chieti in Italy on several occasions. Since 2021, Patrique Fiedler is Junior Professor and Head of the group “Data Analysis in Life Sciences” at the Institute of Biomedical Engineering and Computer Science at the Technical University Ilmenau. His research interests include data fusion, analysis of multimodal datasets and body sensor networks, as well as the exploration of novel sensor concepts for biomedical engineering. Moreover, a focus is the development of online-capable analysis methods for close-to-sensor data processing.
Dry electrodes for electroencephalography (EEG) enable studies in ecological environments, social interaction, brain computer interfaces, and neurofeedback. Reported channel reliability, preparation time, and wearing comfort vary significantly between differing dry electrode concepts and caps. We investigate applicability, performance, and comfort reported in a multi-center multi-operator study using Multipin Ag/AgCl dry electrodes. We compare the performance of dry Multipin EEG caps and gel-based EEG caps, comprising 64 channels each, in recordings of resting state EEG with open and closed eyes, eye blinks, and visual evoked potentials. Equivalent studies were carried out in six countries involving overall 115 healthy volunteers. We compare electrode-skin impedance, channel reliability, and subject comfort reports between cap types. Furthermore, we investigate the eventual impact of operator experience and preparation time on the performance metrics. The average impedances of the dry EEG caps are four to ten times higher than those of gel-based EEG electrodes. The average channel reliability of the dry electrodes is 15 to 20 % lower than for gel-based electrodes. No considerable differences were observed between the gel-based and dry electrode EEG recordings after exclusion of bad channels. The preparation time of the dry caps is considerably reduced, and the comfort is slightly reduced. All findings are in line with previous publications, but exact values vary considerably between operators. The considerable variability of the performance metrics across operators is suggesting a strong influence of operator training and experience.