Editorial commentary on special issue of Advances in EEG Signal Processing and Machine Learning for Epileptic Seizure Detection and Prediction.

Larbi Boubchir

doi:10.7555/jbr.34.20200700

Abstract

This special issue of The Journal of Biomedical Research features novel studies on epileptic seizure detection and prediction based on advanced EEG signal processing and machine learning algorithms. The articles selected present important findings including new experimental results and theoretical studies.

Highlights

The main goal of this special issue is to solicit original contributions with focus on recent advances in EEG signal processing and machine learning for seizures detection and prediction
Ben Slimen et al reported "EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms"[1]
The authors propose a robust automatic method for EEG epileptic seizure detection and classification based on Dual-tree complex wavelet transform for feature extraction with supervised learning algorithms

Summary

Introduction

The main goal of this special issue is to solicit original contributions with focus on recent advances in EEG signal processing and machine learning for seizures detection and prediction. Ben Slimen et al reported "EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms"[1]. The authors propose a robust automatic method for EEG epileptic seizure detection and classification based on Dual-tree complex wavelet transform for feature extraction with supervised learning algorithms.

Results

Conclusion