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assessments, neuroimaging (EEG and/or fMRI), oxytocin quantification in plasma/saliva, and pharmacological interventions. Part 1 involves the continuation and completion of an ongoing MRC-funded clinical study
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. Electroencephalography (EEG) has emerged as a non-invasive and economical alternative technique for studying neurological disorders. However, current approaches rely primarily on single-channel or pairwise connectivity
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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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, 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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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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experience in multivariate time series analytics, or multi-resolution, or multi-timescale analytics • Experience in collecting EEG or wearable data from participants • Experience in running experiments