Lenka Lhotská

On more than 1.1 million annotated rat EEG/EMG segments, a robustness-oriented deep learning pipeline reaches 94.3% F1 for four-class sleep staging, 7.3 and 6.4 points above SlumberNet's 87.0% and random forest's 87.9%. Most automated stagers were evaluated on random splits that preserve the training distribution, and degrade when subjects or drug conditions shift. The pipeline combines a global context vector, supervised contrastive learning and a dual-branch classifier to learn representations insensitive to subject and treatment; trained on saline recordings only and tested on unseen animals, every metric drops by less than 1%.

On more than 1.1 million annotated rat EEG/EMG segments, a robustness-oriented deep learning pipeline reaches 94.3% F1 for four-class sleep staging, 7.3 and 6.4 points above SlumberNet's 87.0% and random forest's 87.9%. Most automated stagers were evaluated on random splits that preserve the training distribution, and degrade when subjects or drug conditions shift. The pipeline combines a global context vector, supervised contrastive learning and a dual-branch classifier to learn representations insensitive to subject and treatment; trained on saline recordings only and tested on unseen animals, every metric drops by less than 1%.