The 1st International Workshop on Machine Learning for EEG Signal Processing (MLESP 2018)

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Overview

EEG signal processing involves the analysis and treatment of the electrical activity of the brain measured with Electroencephalography, or EEG, in order to provide useful information on which decisions can be made. The recent advances in signal processing and machine learning for EEG data processing have brought an impressive progress to solve several practical and challenging problems in many areas such as healthcare, biomedicine, biomedical engineering, BCI and biometrics. The aim of this workshop is to present and discuss the recent advances in machine learning for EEG signal analysis and processing. We are inviting original research work, as well as significant work-in-progress, covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in EEG data analytics. This workshop is an opportunity to bring together academic and industrial scientists to discuss the recent advances.

The topics of interest include but not limited to:

EEG signal processing and analysis
Time-frequency EEG signal analysis
Signal processing for EEG Data
EEG feature extraction and selection
Machine learning for EEG signal processing
EEG classification and clustering
EEG abnormalities detection (e.g. Epileptic seizure, Alzheimer's disease, etc.)
Machine learning in EEG Big Data
Deep Learning for EEG Big Data
Neural Rehabilitation Engineering
Brain-Computer Interface
Neurofeedback
Biometrics with EEG data
Related applications

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