Alzheimer’s Disease (AD) is the most common neurodegenerative disease. It is typically late onset and can develop substantially before diagnosable symptoms appear. Electroencephalogram (EEG) could potentially serve as a noninvasive diagnostic tool for AD. Machine learning can be helpful in making inferences about changes in frequency bands in EEG data and how these changes relate to neural function. The EEG data was sourced from 2014 paper titled Alzheimer’s disease patients classification through EEG signals processing by Fiscon et al. There were patients with AD, mild cognitive impairment (MCI), and healthy controls. The data was already preprocessed using a fast fourier transform (FFT) to take the data from the time domain to the frequency domain. There were differing levels of effectiveness in terms of classification but generally, Fisher’s discriminant analysis (FDA), relevance vector machine, and random forest approaches were most successful. Due to inconsistent feature importances in different models, conclusions about important frequency bands for classification were not able to be made at this time. Similarly, different frequencies were not able to be localized to different regions of the brain. Further research is necessary to develop more interpretable models for classification.

Classification of Preclinical-Alzheimer's risk group from EEG data and psychological testing data using machine learning.

EEG-based machine learning classification of Alzheimer's Disease and Frontotemporal Dementia using frequency-domain analysis.

Classification of Alzheimer's Disease in mixed EEG dataset

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Classification of Alzheimer's and Healthy subjects from Turkey EEG data

Official code of the paper "Alzheimer’s Disease Classification from EEG using a Multiscale Temporal Deep Network"

Alzheimer's Detection Using EEG Data A project focused on applying machine learning techniques to EEG (Electroencephalogram) data for detecting Alzheimer's disease. The analysis involves signal processing, feature extraction, and classification using advanced algorithms. This repository provides the code and methodology for replicating the results.

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This project aims for classification of Alzheimer's Disease using EEG (Electroencephalogram) signals data and machine learning techniques.

This is a final project submission for Data Science Workshop, Semester1

This project is an Alzheimer's classification using EEG and genotypes to determine the risk for Alzheimer's.

Electroencephalogram Signal for three-class classification of Alzheimer's Disease, Mild Cognitive Impairment and Healthy Ageing

This repo contains code for classification of alzheimer's disease from EEG signal.

A fuzzy based CNN classification of EEG data for classification of Alzheimer's disease and Parkinson’s disease.

MSE-based EEG complexity analysis for Alzheimer's Disease and FTD classification - Python + R

OpenNeuro dataset - Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification

Code accompanying the preprint Alzheimer’s Disease Stage Classification Using Fused Hand-Drawing and EEG Signals.

This project explores neural‑network–based EEG classification for early detection of Alzheimer's disease, leveraging graph neural networks (GNNs)

Classification of Alzheimer's , Frontotemporal Dementia and healthy controls by EEG signals. The signals are obtained from an Openneuro 2023 dataset.

A deep learning-powered pipeline for automated classification of Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), and Cognitively Normal (CN) individuals using EEG recordings.

"AI-Powered Prediction of Alzheimer's, Parkinson's, and Seizures using Earbud-Collected EEG Data. Features advanced ML models for EEG signal processing, disease classification, and time-frequency analysis, enabling accurate and accessible health monitoring."

Alzheimer's Detection Using EEG Data A project focused on applying machine learning techniques to EEG (Electroencephalogram) data for detecting Alzheimer's disease. The analysis involves signal processing, feature extraction, and classification using advanced algorithms. This repository provides the code and methodology for replicating the results.

Designed an EEG-based Alzheimer's Disease classification pipeline integrating spectral feature extraction, PCA/autoencoder dimensionality reduction, and multi-classifier benchmarking on a dataset of 848,640 recordings.

This project analyzes EEG recordings and participant data to explore potential biomarkers for Alzheimer’s disease. It extracts brainwave activity in key frequency bands from raw EEG signals and combines these neural features with demographic and cognitive test data for classification. Using a Random Forest classifier.

This repository contains the computational pipeline developed for the simulation and analysis of brain dynamics associated with Alzheimer’s disease progression. The project integrates structural connectivity, neural mass modeling, simulated M/EEG signals, feature extraction, and machine learning classification.

Investigated inter-hemispheric asymmetry and functional connectivity (Pearson correlation and Phase Locking Value) in a large-scale 848,640-sample EEG dataset to identify disrupted brain network patterns associated with Alzheimer’s Disease using statistical analysis and machine learning classification.