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The ultimate objective of this project is to detect cognitive strategies in single-trial EEG data. In very broad terms, we will use the HMP method (https://github.com/GWeindel/hmp ) to discover
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is to detect cognitive strategies in single-trial EEG data. In very broad terms, we will use the HMP method (https://github.com/GWeindel/hmp ) to discover cognitive operations in existing data, label
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the individual and central projects of CRC1451. The rich data collection contains behavioral and kinematic data of humans and mice (locomotion); fMRI, EEG and TMS data from healthy subjects and patients (with
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MEG or EEG), strong skills in artificial neural netwroks, strong programming skills (MATLAB and/or Python), and solid knowledge of statistical methods. Experience in designing, fitting and comparing
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scientific writing and communication skills. Experience with one or more of the following will be particularly valued: Experience with BESA (EEG) and/or Satori (fNIRS) Physiological signals such as EEG, fNIRS
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visual signals such as gestures and lip movements. Working with advanced EEG and MEG methods, naturalistic stimuli and innovative analytical approaches, you will help unravel the neural dynamics
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-driven intracranial EEG (iEEG) approaches to characterize the Epileptogenic Network (EpiNet) and identify network chokepoints involved in seizure initiation. The position integrates preclinical optogenetic
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who maintain exceptional cognitive and physical function. We aim to understand the links between vascular brain function, neural plasticity measured through EEG, and biomechanical control of standing
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America, and sub-Saharan Africa. In addition, you will contribute to a multi-methods project including artificial language learning and EEG, as well field-based experiments with speakers of Cha’palaa
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to work in an interdisciplinary team. At least 1 year of experience in research activities carried out within at least one project in the field of brain research, including experience in EEG data recording