SmartEEG: An End-to-End Framework for the Analysis and Classification of EEG signals

EEG analysis frameworks are scientific tools to make neuroscientists' research less cumbersome. However, the focus has been on manual feature extraction and signal localization with limited machine classification functionality. In this paper, we propose a new framework for developing deep learning models suited for analyzing and classifying EEG signals. To provide a baseline for comparison, after training FCNNs, CNNs, and RNNs, we show that deep learning models have more potential for general EEG monitoring than classical algorithms. Furthermore, their performance can be independent of equipment and patient.