A single-channel EEG classification system for multiscale characterization of mouse vigilance state

Long-term analysis of mouse sleep is constrained by the dependence of conventional scoring on expert interpretation of electroencephalographic (EEG) and electromyographic (EMG) recordings. We developed a channel-agnostic, EEG-only framework that combines cross-animal sleep-stage classification, causal temporal organization, and probabilistic hypnodensity analysis from a single cortical EEG signal. Motor, somatosensory, and visual cortical recordings were treated independently by a convolutional–recurrent classifier, and generalization was evaluated using nested leave-one-mouse-out cross-validation in eight mice, with each test animal excluded from training, normalization, and model selection. The primary model achieved 0.897 ± 0.058 accuracy and 0.856 ± 0.076 macro-F1 across previously unseen animals while preserving the principal features of expert EEG/EMG-supported sleep architecture. Causal temporal smoothing reduced fragmented predictions and restored physiologically coherent episode durations, counts, and transition structure. Beyond categorical staging, the 4-s causal EEG window was advanced in 1-s steps to generate continuous Wake, NREM, and REM hypnodensity profiles. This representation preserved overall classification performance while revealing increased probability ambiguity and state mixing around expert-defined sleep transitions. The framework was subsequently deployed without supervised adaptation in six additional mice with 32–33 recorded days per animal, where it retained organized daily sleep architecture and probabilistic sleep structure over extended recordings while remaining sensitive to changes in recording conditions. Together, these results establish a single-channel EEG framework for robust cross-animal sleep staging, physiologically structured long-term analysis, and second-by-second characterization of sleep-state probabilities in mice.