Decoding neuronal criticality firing patterns for large brain based EEG models.

The critical brain hypothesis suggests that the brain operates near critical points, balancing order and disorder for optimal processing via self-organized neural firing patterns. However, important gaps remain in understanding criticality in human in vivo brain dynamics using electroencephalography (EEG), particularly in translating these systems neuroscience concepts to operational applications (e.g., cognitive state detection, human-machine teaming). We address three key aspects that yet remain to be demonstrated: (1) how to decode large-scale EEG neural recordings coordination and self-organization of distributed neuronal populations as they evolve toward critical dynamics; (2) how windowed EEG neural events during cognitive tasks exhibit signatures of criticality; and (3) how decoding randomly selected or task-irrelevant null events fails to recover critical dynamics ruling out artifacts of noise that mimic scale invariance. To address these issues, we introduce EEG techniques that identify multiple interacting spectral oscillatory groups' self-organization through unique hierarchical arrangements utilizing an optimized filter bank-based approach while performing cognitive tasks. This EEG neuronal decoding method characterizes signals across 4-dimensions of intensity, phase, time, and frequency that are represented as a hierarchical rank. This allows us to observe how the brain's self-organization across numerous neuronal oscillatory bands during cognitive tasks, confirming criticality by fitting a power law estimate over a range of k≈[102,105]. Supplementing these findings, we demonstrate that during non-task periods, the power law fails to fit, indicating the absence of scale-invariant criticality (k≈[102,102]), showing that criticality emerges during cognitive tasks when the neuronal firing is sent to the outer cortex of the brain.