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EEG from bench to bedside: Conventional electrophysiological biomarkers and applied deep learning in Psychiatry
Sebastian OlbrichJan. 16
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Oscillatory Brain Activity and the Deployment of Attention
John J. Foxe, PhDJan. 16
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Jan. 16
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Non-Invasive Remote EEG Monitoring at Home in Epilepsy: Insights from the EEG@HOME Study
Dr. Andrea BiondiJan. 16
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To be announced
Prof. Giorgio di LorenzoJan. 16
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Contribution of new methods for combined EEG/MEG source analysis and optimized mc-TES to focal medication-resistant epilepsy
Prof. Dr. Carsten WoltersJan. 16
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Jan. 16
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Decoding Social Touch: EEG Signals Reveal Interdependent Somatosensory Pathways Relevant to Human Affect
Prof. Dr. Annett SchirmerJan. 16
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Jan. 16
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Mechanisms Behind Neurotechnology-Assisted Rehabilitation: First Results from a Double-Blind Randomized Controlled Trial
Reinhold Scherer, PhDJan. 16
Prof. Dr. Claudia Gianelli is an Associate Professor of General Psychology at the Department of Clinical and Experimental Medicine, University of Messina (Italy). After a PhD in Cognitive Neuroscience (Bologna-Lyon) she was first a Research Scientist at the University of Potsdam in Germany and then a Research Fellow at the University School for Advanced Studies IUSS Pavia in Italy. Her main research interests are in the field of motor cognition investigated by means of neurophysiological and behavioral measures (TMS, EEG, motion tracking), both as single methods and in combination, in healthy participants and clinical populations.
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Dr. Elena Cesnaite completed her doctoral work at Max Planck Institute for Human Cognitive and Brain Sciences, where she worked on large open-access EEG datasets to investigate the link between resting-state neural dynamics and future cognitive performance. This work sparked her interest in best practices for EEG data analysis in large neuroimaging studies. Currently, Elena is a postdoctoral researcher collaborating with Prof. Niko Busch at the Institute of Psychology, University of Münster, focusing on the EEGManyPipelines project, a many-analyst study investigating variability in EEG processing approaches and their implications for the results.
When analyzing electroencephalographic (EEG) data, researchers face a maze of potential methods and analytical approaches. But how much do these choices actually influence the results? The EEGManyPipelines (EMP) project addressed this question through a large-scale collaborative effort to test the robustness of EEG findings across different analysis strategies. In this unprecedented study, a single EEG dataset and set of research questions were provided to 168 expert teams worldwide. Each team applied their own methods to analyse this dataset, later submitting preprocessed data, analysis scripts, and hypothesis testing results, to the EMP. Our findings show that while EEG processing pipelines vary widely, two steps—the baseline window length and the approach to multiple comparisons—were strongly associated with whether significant effects emerged between conditions. Further analysis of the preprocessed data revealed that choices such as reference type and the use of plugins for filtering bad components significantly impacted the magnitude of these effects. The expected impact of these results, as well as implications for future research, will be discussed.