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The Many Faces of Fear: Univariate, Predictive and Representational Perspectives on Fearful Neuroimaging

This project explores how different fMRI analysis methods reveal distinct aspects of the neural representation of fear, including a mass univariate approach (GLM), a machine learning (decoding) approach, and representational similarity analysis (RSA) approach. While GLM identified some expected activation patterns and machine learning failed to decode fear ratings reliably, RSA revealed modest but significant structure in frontal regions, highlighting the value of methodological triangulation in cognitive neuroscience.

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EEG-Based Odor Preference Modeling 🌹🧀️🪷🍃

The human sense of smell plays a crucial role in emotional experience. Previous research has shown that EEG can distinguish between pleasant and unpleasant odors at an individual level (Kroupi et al.,2014), but the consistency of these preferences across individuals remain open questions. OPPD dataset: www.epfl.ch/labs/mmspg/downloads/page-119131-en-html

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Tulpas: invisible friends in the brain.

Tulpas are invisible friends that can be cultivated on will by so called Tulpamancers. This fMRI dataset comprises scans of Tulpamancers, comparing periods where there is an experiential presence of such Tulpa and where there is not. The aim of this proejct is to study the neurophysiological signature of Tulpas using GLMs, functional connectivity measrues, machine learning, and deep neural networks. website.

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