Projects

This project utilizes fMRI data and machine learning to predict sleep states, aiming to enhance understanding of sleep patterns and disorders. By analyzing brain activity during different sleep stages, it seeks to improve diagnostics and develop personalized treatments for sleep disorders. The primary goal is to determine whether a participant is asleep or awake using resting-state fMRI data.

Brain Decoding Using Connectivity Informed Models

By Arian Morteza

June 15, 2024

Brain Decoding is the reconstruction of the sensory and other stimuli form the information that has already been encoded and represented in the brain. For example, image genration, and task classification, from brain activity signals could be covered under this topic. In this project, a graph neural netwrok appriach is used to learn the representation of the brain regions activities, and do image classification for the end-user (patient).

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Detecting ADHD through fMRI signals using ML classification models

By Agustín Rodriguez, Ezequiel Sirne, & Francisco Covelli

June 14, 2024

We used the ADHD-200 Sample dataset to implement various machine learning classification models, aimed at diagnosing ADHD through resting-state fMRI signals.

Detection of Alzheimer's through Acoustic and Semantic Markers

By Matías Caccia, Jeremías Inchauspe, & Paloma Georgopulos

June 14, 2024

Our project aims to identify acoustic and semantic markers from the speech of Alzheimer’s patients to detect the disease and estimate MMSE scores using machine learning models. This approach offers a scalable and cost-effective method for early diagnosis.

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Regression-Modelling ERPs (and More!)

June 11, 2023

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.

Do you feel the words? An fMRI analysis on tactile vs non-tactile words in Mandarin Chinese

By Yusuke Taira

June 10, 2023

Language consists of many thousands of words which differ in meaning and syntactic category. Some words may refer to reletevely touchable, hence concrete aspects of the world while others are abstract in meaning which do not have physical references. But which brain areas are associated with tactile word processing? This project aims to investigate brain activation of participants when listening to tactile vs non-tactile word stimuli. The preliminary result shows that large areas of parietal lobe is particularly activated to tactile words compared to non-tactile words

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Multimodal Investigation of Neural Correlates of Athletic Performance

By Arunim Guchait

June 10, 2023

This project investigates the neural correlates of athletic performance using fMRI, dMRI, and FSLVBM to compare grey matter volume and white matter connectivity between athletes and non-athletes. The study aims to identify brain regions associated with athletic performance, explore white matter connectivity differences, and examine the relationship between brain structure and specific athletic skills. The dataset included nine Indiana University football players and nine controls.

Predicting feedback perception in an online language learning task using EEG and machine learning

By Dylan Scott Low Y.J., & Stephanie Fu Pei-Yun

June 10, 2023

In this project, we aim to use machine learning on EEG data from participants’ language learning tasks on Duolingo. Specifically, we ask if EEG features can predict whether the participant has gotten a task right or wrong when they receive feedback. Using a k-nearest neighbours classifier, we achieve 98% accuracy in determining correct or incorrect answers based on EEG voltages from 8 electrodes.

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Analyzing variability of working memory and reward processing in children with and without ADHD using fMRI data

By Zara Khan, Fariah Sandhu, & Clara Sun

June 9, 2023

The focus of our project was to gain experience using neuroimaging tools to preprocess, analyze, and visualize functional MRI data. We aimed to explore differential variability in brain connectivity among children with and without ADHD. Project reports are incorporated on the BHS website.

Comparing different methods for mice behavioral analysis

By Pilar López Maggi, Gonzalo Giordano, Ana Pavlova Contreras, & Santiago D'hers

June 9, 2023

Our project aims to develop different methods for the analysis of behavior in mice (in this case, exploration of an object) to determine which is the best approach to this kind of study. We were able to implement and compare three increasingly complex methods to determine exploration time: manual labeling; motion tracking and data analysis using a custom algorithm; training a Machine Learning classifier on our labeled data.

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Sleep detection using fMRI data

Brain Decoding Using Connectivity Informed Models

Detecting ADHD through fMRI signals using ML classification models

Detection of Alzheimer's through Acoustic and Semantic Markers

Regression-Modelling ERPs (and More!)

Do you feel the words? An fMRI analysis on tactile vs non-tactile words in Mandarin Chinese

Multimodal Investigation of Neural Correlates of Athletic Performance

Predicting feedback perception in an online language learning task using EEG and machine learning

Analyzing variability of working memory and reward processing in children with and without ADHD using fMRI data

Comparing different methods for mice behavioral analysis

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