AUTHOR=Ellis Charles A. , Miller Robyn L. , Calhoun Vince D. TITLE=A Systematic Approach for Explaining Time and Frequency Features Extracted by Convolutional Neural Networks From Raw Electroencephalography Data JOURNAL=Frontiers in Neuroinformatics VOLUME=16 YEAR=2022 URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2022.872035 DOI=10.3389/fninf.2022.872035 ISSN=1662-5196 ABSTRACT=
In recent years, the use of convolutional neural networks (CNNs) for raw resting-state electroencephalography (EEG) analysis has grown increasingly common. However, relative to earlier machine learning and deep learning methods with manually extracted features, CNNs for raw EEG analysis present unique problems for explainability. As such, a growing group of methods have been developed that provide insight into the spectral features learned by CNNs. However, spectral power is not the only important form of information within EEG, and the capacity to understand the roles of specific multispectral waveforms identified by CNNs could be very helpful. In this study, we present a novel model visualization-based approach that adapts the traditional CNN architecture to increase interpretability and combines that inherent interpretability with a systematic evaluation of the model