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By Ngieng Shih Yang
June 16, 2025
The purpose of the project is to compare various predictive models to compare the effectiveness of each predictive model and to identify important features that best contribute towards predicting general cognition. Additionally, the ideal number of features were also explored for each model.
By Hu Ding Xuan
June 15, 2025
This study examines dorsolateral prefrontal cortex (dlPFC) and posterior parietal cortex (PPC) connectivity and dlPFC BOLD time series in ADHD versus typically developing (TD) children during the cued stop-signal task (CSST) using fMRI data from OpenNeuro ds005899. It is hypothesised that stronger dlPFC-PPC connectivity will be found in the ADHD group.
By Xu Peng
Implementing Time-Resolved Representational Similarity Analysis to Track Cortical Representation of Consciousness Report
By Wei-Xuan Chai
This project was conducted as part of Brainhack School 2025. It aimed to classify major depressive disorder (MDD) using temporal-domain EEG features (i.e., band power), applying both machine learning (SVM) and deep learning (EEGNet) models.
By Wei-Chen Huang
This project examines how language-related brain regions connect with DMN, FPN, and SN during rest, using fMRI data to explore links between functional connectivity and language comprehension.
By Truc (Curt) Nguyen
June 14, 2025
This mini-project for the 2025 Brainhack School is part of my PhD dissertation on Late-Life Cognitive Heterogeneity, where I examine the neural correlates of cognitive dispersion – a measure of within-individual variability – using neuropsychological and fMRI data from the Midnight Scan Club dataset (OpenNeuro ds000224).
By Fabiana Ojeda 歐瑩忻
This project explores how deviant auditory tones in a cross-modal oddball paradigm elicit a stronger P300 component using EEG data from the MNE sample dataset. The analysis focuses on ERP comparison and difference waves, setting the stage for future investigations on emotional modulation of P300.
By Stella Ruddy
This project aimed to explore tools and techniques used to analyze fMRI brain activation at the first level using data from an open-access dataset. We produced a comprehensive Jupyter Notebook that provides a step-by-step guide to running the analyses, including applying the GLM, defining contrasts, and generating various brain activation maps.
By Wang Chi
This project explores how the brain responds to natural stories via TRF modeling on the SMN4Lang dataset, using acoustic envelope and word-aligned features – and includes an exploratory attempt at word classification using machine learning.
By Tzu-Yun Kung
This project aims to examine how the brain supports two types of inference—active and passive—during the process of posterior belief integration. I applied trial-by-trial analysis to track participants’ learning trajectories over time.
This is the project gallery from past editions.
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147 participants have uploaded their projects.
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