DFA criticality features from EEG enable Naive Bayes to classify deep sleep with 87.17% balanced accuracy, supporting passive BCI neurofeedback.
Robotic and virtual reality BCIs using spatial tactile and auditory odd- ball paradigms,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
q-bio.NC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Deep Sleep Classification via EEG Signal Criticality: A Passive BCI Approach for Sleep-Improvement Neurofeedback
DFA criticality features from EEG enable Naive Bayes to classify deep sleep with 87.17% balanced accuracy, supporting passive BCI neurofeedback.