Thesis project: "On the clinical acceptance of EEG seizure prediction methodologies". Explainability of seizure prediction models.

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

American Epilepsy Society Seizure Prediction Challenge.

Computational Artificial Intelligence Projects (Seizure Detection) - Fall-2022

kaggle competition: seizure prediction

Hidden Markov Models for Epilepsy Data

Seizure prediction contains innovative methods using adaptive filtering for detecting epileptic seizures based on energy signals

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…

Real-time Forecasting Epileptic Seizure using EEG

Epilepsey and Insomnia Detection Using EEG signals

The codebase for a research project that uses common spatial patterns (CSP) filters to search for waveforms in epileptic Electrocorticographic (ECoG) signals that are discriminative of the preictal and interictal state.

The code for the paper "The goal of explaining black boxes in EEG seizure prediction is not to explain models’ decisions", published in Epilepsia Open (https://doi.org/10.1002/epi4.12748). It concerns explainability methods on Machine Learning for EEG seizure prediction.

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

Epileptic EEG detection using the linear prediction error energy

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.

on-line seizure prediction by evolve neuro-fuzzy model based on SOP and SPH

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

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

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