Quantifying consciousness through intrinsic probability density function.
Biological psychology September 1, 2025 Norden E Huang, Wei-Shuai Yuan, Albert C Yang et al.
The intrinsic probability density function (iPDF) is a new quantitative method for evaluating dynamic interactions between brain regions that underlie conscious states. The method analyzes EEG signals by decomposing them into intrinsic mode functions and generating scale-dependent probability density functions that capture subtle variations in neural modulation. Testing across general anesthesia, sleep stages (wakefulness, REM, deep sleep), sensory conditions (eyes open vs closed), and between dementia patients and healthy subjects showed that active neural interactions during wakefulness and REM sleep produce super-Gaussian iPDF patterns, while reduced interactions during anesthesia and deep sleep yield near-Gaussian profiles. A classification model using iPDF features achieved approximately 87% accuracy in distinguishing dementia patients from healthy controls, suggesting iPDF as a potential clinical biomarker.