What does my network learn? Assessing interpretability of deep learning for EEG.

ABSTRACT: Electrophysiological studies are profiting from multivariate pattern analysis methods. However, these mostly rely on machine-learning algorithms that assume consistent response latencies across trials and individuals. Deep learning provides high performance without such assumptions, but often at the cost of interpretability of learned features. Here, we evaluated how the interpretability of deep learning for electroencephalography (EEG) data is affected by preprocessing choices, the network's architecture, and the way the learned features are extracted and visualized. We trained two convolutional neural networks (CNN): (1) ResNet, a residual network, and (2) EEGNet, which leverages spatiotemporal properties of EEG. We trained these networks to decode single-trial EEG responses to three dif

SUBMITTER: Goktepe-Kavis P 

PROVIDER: S-EPMC12673218 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature