Discover this project
By Timothy Tan
June 9, 2025
An attempt to replicate the study by Soler-Vidal et al. (2022), using the study’s dataset available on OpenNeuro. Attempted preprocessing of the first participant, sub-01, using FSL (FEAT files in ‘sub-01’ folder) and fMRIPrep (material found in ‘code’ and ‘derivatives’ folders). Attempted creation of timing files, found in ‘Ideal_Time_Series’ folder.
By Nilay Ozdemir Haksever
June 3, 2025
Firstly, this project investigates differences in frontoparietal brain connectivity between individuals diagnosed with ADHD and control participants during Spatial Working Memory Task, using fMRI-based connectivity data. In the second part of this project, To classify individuals as either having ADHD or being in control group based on functional connectivity data features machine learning models was tested by using k-fold cross validation.
By Joel P. Diaz-Fong, Lucas Vidal Murakami, & Aijia Ivy Zhong
May 20, 2025
Accurate EEG source localization depends on precise electrode coordinates, yet current methods are often manual, costly, and technically demanding. Deep Electrode Mapper attempts to address this by applying deep learning to segment electrodes from 3D head models—derived from MRI or 3D scans—and localize their coordinates using clustering. Although the project remains incomplete, it demonstrates a proof-of-concept pipeline, and progress is documented in the public repository.
By Thomas Dagonneau
May 16, 2025
This project presents an open-source pipeline for segmenting multiple sclerosis lesions in the spinal cord using multimodal MRI data. Built for the MS-Multi-Spine Challenge, it combines nnUNet, the Spinal Cord Toolbox, Docker, and Boutiques for reproducibility and ease of use. The pipeline includes preprocessing, inference, and post-processing steps, and is packaged with full documentation and containerization to support future research and clinical applications in spinal cord imaging.
By Kuan-Yu Chen, Ruo-Chi Yao, Liang-Mei Lin, & Ming-Feng Hsin
June 25, 2024
In our study of three participants, removing the alpha band affected TRFs, with some features being suppressed and others enhanced. This simplification highlighted local signals, making brain activity clearer. However, it’s unclear if these enhanced signals represent true brain activity or noise, requiring further analysis for validation.
By Katia Djerroud
Analyzes emotional dynamics in social interactions using voice and linguistic analysis on the Friends TV show dataset. Utilizing tools like Praat, OpenSmile, NLTK, and Hugging Face Transformers, it detects emotions from audio and text. Deliverables include code, documentation, datasets, and analysis workflows. Achievements encompass integrated emotion detection models and a robust analysis pipeline, with future plans to enhance models and expand datasets.
By Iangola Andrianarison
June 21, 2024
This project trains machine learning classification models to make predictions of adhd diagnosis from brain fRMI connectivity measures which are obtained from a resting state ADHD dataset . The main goals of this project are to get more practice with machine learning tools and to learn how work with brain data more precisely fMRI data.
By Cornelius Crijnen
This project aims to evaluate the similarity between the representations of artificial neural networks (ANNs) and the visual system in the mouse brain using Representational Similarity Analysis (RSA).
By Vincent Chamberland, & Renaud Dagenais
This project aims to develop a preprocessing pipeline to fine-tune a Video Large Language Model (Vid-LLM) for automatic annotation of gameplay recordings in cognitive neuroscience studies. Leveraging the Gym Retro ecosystem and the Courtois NeuroMod dataset, we convert event logs into video format and generate detailed annotations and timestamps to train the Vid-LLM. Deliverables include cleaned datasets, documentations and Jupyter notebooks.
By Y Song
While Parkinson’s disease (PD) is recognized by its motor symptoms, it is also characterized by non-motor symptoms (NMS), such as anxiety, depression, pain, etc. NMS often precede cognitive decline, making them potential predictors of such decline. This project aims to investigate the longitudinal association between non-motor symptoms and cognitive and neural decline in patients with PD. Early identification of individuals at higher risk of cognitive decline through their NMS presentation can facilitate timely interventions.
This is the project gallery from past editions.
97 projects are currently displayed.
150 participants have uploaded their projects.
Copyright (c) 2025, BrainHack School; all rights reserved.
Template by Bootstrapious. Ported to Hugo by DevCows.
