Deep Sleep Classification via EEG Signal Criticality: A Passive BCI Approach for Sleep-Improvement Neurofeedback
The study used Detrended Fluctuation Analysis (DFA) of EEG signals to extract features related to neural criticality during sleep. These features were then used with a Naive Bayes classifier to automatically classify deep sleep stages. The method achieved high accuracy, reaching 87.17% balanced accuracy, proving that analyzing how brain signals scale provides a robust way to monitor deep sleep passively.