LSTM Autoencoder for classification of artifact-ridden EEG epochs

EEG signals frequently contain artifacts, which hinders analysis and requires time-intensive artifact removal. In this study, seeking to improve automation, we introduce an LSTM Autoencoder designed for EEG epoch classification through the anomaly detection approach. Its performance is benchmarked against two state-of-the-art denoising Convolutional Autoencoders. The implemented network exhibits high classification performance of clean and noisy epochs and additionally facilitates signal quality interpretation.