Machine Learning

Decoding Perceived Emotion from BOLD data using Machine Learning

June 12, 2025

This project applies machine learning to decode perceived emotions from fMRI data using ROI-based features. Data from the ds003548 OpenNeuro dataset are analyzed, with task labels extracted from events files. ROI time series are extracted using the MIST 64-ROI atlas, and mean signals during emotion blocks are classified using linear SVM. The goal is to distinguish between six conditions (happy, sad, angry, neutral, blank, scrambled), demonstrating key concepts and challenges in neuroimaging-based classification.

Functional Connectivity in ADHD: Group Differences and Predictive Modeling During Spatial Working Memory Task

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.

ADHD diagnosis prediction using machine learning

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.

Evaluating ANNs of the Visual System with Representational Similarity Analysis

June 21, 2024

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).

Grandpa is moody, will his cognition decline? Predicting cognitive decline in Parkinson's disease from non-motor symptoms evolution

June 21, 2024

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.

Detecting ADHD through fMRI signals using ML classification models

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.

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

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.

Comparing different methods for mice behavioral analysis

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.

Schizophrenia prediction: use of Neuroimages and Artificial Intelligence Models

June 9, 2023

Schizophrenia (SZ) involves significant alterations in perception, thoughts, mood, and behavior. This project aims to develop an AI model using machine learning for complementary SZ diagnosis, utilizing prefrontal cortex connectomics and tractography techniques. It focuses on creating scripts for data separation, comparing classification models, and analyzing the connectome of healthy individuals and those with SZ. Early detection and accurate diagnosis through machine learning will enable targeted interventions, improving outcomes for individuals with SZ.

Using a machine learning model trained on functional connectivity patterns to predict ADHD

June 9, 2023

This project uses functional magnetic resonance imaging data to study the connectivity of children with Attention Deficit Hyperactivity Disorder (ADHD).A set of children diagnosed with ADHD were given a series of memory tasks while undergoing MRI scans. In this project, data from one of these tasks was used to calculate connectivity matrices for 65 subjects from that data set and a machine learning model was trained. The data was downloaded from Openneuro website.

Decoding Perceived Emotion from BOLD data using Machine Learning

Functional Connectivity in ADHD: Group Differences and Predictive Modeling During Spatial Working Memory Task

ADHD diagnosis prediction using machine learning

Evaluating ANNs of the Visual System with Representational Similarity Analysis

Grandpa is moody, will his cognition decline? Predicting cognitive decline in Parkinson's disease from non-motor symptoms evolution

Detecting ADHD through fMRI signals using ML classification models

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

Comparing different methods for mice behavioral analysis

Schizophrenia prediction: use of Neuroimages and Artificial Intelligence Models

Using a machine learning model trained on functional connectivity patterns to predict ADHD

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