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model of human working memory. During the project, we will follow an intertwined model-experimental approach. First, we will use electroencephalography (EEG) experiments to investigate the neural basis
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to measure how young brains track information content within screen media in real-time. The project will involve collecting and analysing EEG, fNIRS and home wearable recordings from babies and children
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project will involve collecting and analysing EEG, fNIRS and home wearable recordings from babies and children. Experience of advanced data analytics, including if possible experience of coding in Matlab
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no research. We propose to leverage state-of-the-art techniques to measure how young brains track information content within screen media in real-time. The project will involve collecting and analysing EEG
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patricia.davies@colostate.edu Position Summary Dr. Patricia Davies’ ICR Grant. The successful applicants will assist in all aspects of the current research which involves collecting EEG data, conducting blood draws
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, stress markers, EEG, and ECG — will be collected by VR headsets and IoT devices. ML algorithms will analyse this data to identify trends, project risk factors, and propose tailored treatments. By combining
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of new EEG and MEG neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be
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electroencephalography (EEG), behavioural tasks, and computational modelling, the student will examine how these interventions influence brain dynamics, cognition, and subjective experience.
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focusing on elucidating neural (e.g., EEG/ERPs, MRI), behavioral, and real-time markers (e.g., passive sensor data, EMA) of depression and suicidal behaviors in adolescents. The position is fulltime
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treatment. Objectives Conduct studies to measure the impact of multisensory stimulation on sensorimotor learning, working memory, and selective attention. Utilise EEG to investigate the neural mechanisms