Non-invasive polysomnography recording on dogs has been claimed to produce data comparable to those for humans regarding sleep macrostructure, EEG spectra and sleep spindles. While functional parallels have been described relating to both affective (e.g., emotion processing) and cognitive (e.g., memory consolidation) domains, methodologically relevant questions about the reliability of sleep stage scoring still need to be addressed. In Study 1, we analyzed the effects of different coders and different numbers of visible EEG channels on the visual scoring of the same polysomnography recordings. The lowest agreement was found between independent coders with different scoring experience using full (3 h-long) recordings of the whole dataset, and the highest agreement within-coder, using only a fraction of the original dataset (randomly selected 100 epochs (i.e., 100 × 20 s long segments)). The identification of drowsiness was found to be the least reliable, while that of non-REM (rapid eye movement, NREM) was the most reliable. Disagreements resulted in no or only moderate differences in macrostructural and spectral variables. Study 2 targeted the task of automated sleep EEG time series classification. Supervised machine learning (ML) models were used to help the manual annotation process by reliably predicting if the dog was sleeping or awake. Logistic regression models (LogREG), gradient boosted trees (GBT) and convolutional neural networks (CNN) were set up and trained for sleep state prediction from already collected and manually annotated EEG data. The evaluation of the individual models suggests that their combination results in the best performance: ~0.9 AUC test scores.

We tested whether dogs and 14–16-month-old infants are able to integrate intersensory information when presented with conspecific and heterospecific faces and vocalisations. The looking behaviour of dogs and infants was recorded with a non-invasive eye-tracking technique while they were concurrently presented with a dog and a female human portrait accompanied with acoustic stimuli of female human speech and a dog’s bark. Dogs showed evidence of both con- and heterospecific intermodal matching, while infants’ looking preferences indicated effective auditory–visual matching only when presented with the audio and visual stimuli of the non-conspecifics. The results of the present study provided further evidence that domestic dogs and human infants have similar socio-cognitive skills and highlighted the importance of comparative examinations on intermodal perception.