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