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An Updated Perspective on the Use of EEG for Diagnosis, Prediction, and Treatment Individualization in Depression
Prof. Giorgio di LorenzoDone
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EEG activity elicited by motor learning and to predict workload under microgravity
Prof. Dr. Elsa KirchnerDone
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Assessing the impact of analytical choices on EEG results: Insights from the EEGManyPipelines project
Prof. Dr. Claudia Gianelli & Dr. Elena CesnaiteDone
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EEG Cloud Clinical Service to proactively objectify diagnoses for Neurodegenerative Pathologies and Disorders Therapy
MSc. Daniel MoreraDone
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.
Authors: Patrique Fiedler1, Ricardo Bruña
Functional connectivity (FC) has been shown to be a promising metric for early detection of detrimental or pathological processes affecting the brain, preceding structural brain changes. Routine measurement of electroencephalography (EEG) and thus regular assessment of FC may provide a tool for constant monitoring of brain health, but requires easy, comfortable and reliable self-application. EEG using dry electrodes may provide the basis for such routine self monitoring applications. We investigate the use of dry electrodes for FC assessment and compare findings between novel dry and conventional gel-based recordings.
We analysed two resting state EEG datasets: a) a low-density (LD-)EEG dataset with 64 channel recordings comparing 10-20 (gel-based) and equidistant (dry) electrode layouts; b) a high-density (HD-)EEG dataset with 256 channels with equidistant layout each. We calculated functional connectivity metrics in sensor and in source space, respectively. In sensor space we calculated the per-channel phase locking value (PLV) and the corrected imaginary part of the PLV (ciPLV). For assessing differences in source space, we used the HD-EEG sensor space data to reconstruct the activity in the source space and estimated the FC in source space using phase synchronization. We compared the results in terms of nodal strength using a paired-samples t-test.
In the LD-EEG, the correlation between both electrode types using either the PLV or the ciPLV is very low (i.e. random) in the theta band, presumably due to low-frequency noise. For the EEG bands above 8 Hz, we found a consistent positive correlation between wet and dry recordings. This observation holds for all compared FC metrics. For the HD-EEG, the results showed no systematic differences between the FC in wet and gel-based recordings when using leakage-corrected metrics, and only a spatial cluster of differences (medial occipital areas, higher FC with gel-based caps) in high frequency bands when using non-leakage-corrected FC.