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.

solution for the American Epilepsy Society Seizure Prediction Challenge

Empirical wavelet transform (EWT) in Python

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

Predict seizures from EEGs using two models of spiking neural networks

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

Forecasting seizures with multiple models, including artificial neural networks

EEG wearable device using CNN for seizure prediction

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

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

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

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

Real-time Forecasting Epileptic Seizure using EEG

based on kaggle's Melbourne University AES/MathWorks/NIH competition

In this research project we used a shift-invariant k-means algorithm to learn a preictal and interictal codebook of prototypical waveforms that can be used to summarize the occurrence of recurrent waveforms and to classify between preictal and interictal segments. We use the common spatial patterns (CSP) method to spatially filter the multichann…

Epilepsey and Insomnia Detection Using EEG signals