The project is about applying CNNs to EEG data from CHB-MIT to predict seizure

Seizure prediction from EEG data using machine learning. 3rd place solution for Kaggle/Uni Melbourne seizure prediction competition.

Empirical wavelet transform (EWT) in Python

solution for the American Epilepsy Society Seizure Prediction Challenge

This repository contains the trained deep learning models for the detection and prediction of Epileptic seizures.

EEG wearable device using CNN for seizure prediction

Epilepsy Prediction with CNN-BiLSTM | BSc dissertation project

Predict seizures from EEGs using two models of spiking neural networks

Code and data of the paper "Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm", published by Scientific Reports in 2022.

1st place algorithm from Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge

2nd place algorithm for Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge

Forecasting seizures with multiple models, including artificial neural networks

Kaggle - Melbourne University AES/MathWorks/NIH Seizure Prediction 2016 competition - predict seizures in long-term human intracranial EEG recordings

This project focuses on predicting epileptic seizures using EEG signals and ensemble learning techniques. It aims to provide accurate and timely predictions to help individuals with epilepsy manage their condition more effectively.

Epileptic EEG detection using the linear prediction error energy

3rd place algorithm of the Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge

Code and data of the paper "A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction", published by Scientific Reports in 2021.