An automated tool that trains a multimodal classification model (EEG + fMRI) to predict phenotypic variables (sex, age, diagnosis) and assess whether fusing both modalities improves prediction compared to each modality alone.
This project investigates how resting-state functional connectivity differs between sleep and wake states using simultaneous EEG and fMRI data from the OpenNeuro “Simultaneous EEG and fMRI signals during sleep from humans” dataset. By comparing default mode network connectivity and EEG frequency-band connectivity across states, the project found minimal differences at the network level but a reliable increase in theta-band connectivity during sleep, highlighting the value of combining modalities to characterize state-dependent brain connectivity.
In this project we use EEG patterns to predict treatment responses for individuals with ADHD across different age groups. Project reports are incorporated in the BHS website.
Using EEG band power to investigate cognitive states during golf swings and correlate them with subjective performance ratings.
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.
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.
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
This project investigates whether there are age-dependent EEG patterns for individuals with ADHD and whether these patterns can predict neurofeedback treatment response. Using the ADHD samples from TDBrain database (n=204), we developed a random forest model to characterize age-related EEG biomarkers and assess treatment prediction across different age groups. Our model achieved AUC=0.865, identifying key EEG signatures including theta-beta ratios and frontal low-frequency patterns that vary with age and treatment response.
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.
Emotion perception is contextualized. However, how emotional context modulates word processing is unclear. We regression-fitted raw EEG data to test for emotional valence effects. The results revealed a widespread effect of context valnece, as well as a plausibility N400 waveform, well replicating the past ERP literature. Moreover, as we plan on conducting a subsequent experiment to follow up on the findings the present study has revealed, this project also includes the code for constructing experimental stimuli.
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