Neural Signatures of Consciousness: Non-Linear Dynamics of EEG Responses to Sound

Conscious auditory processing is characterized by higher entropy and more complex recurrence patterns in EEG signals than non-conscious or reduced-consciousness states. Non-linear EEG metrics, including entropy measures, recurrence quantification analysis, and fractal dimension estimation, reliably discriminated between wakeful and subdued neural conditions with classification accuracy exceeding 85%. These findings suggest that non-linear EEG metrics can serve as objective markers of consciousness, beyond traditional frequency-based analyses, paving the way for improved assessment tools in clinical and neuroscientific settings.

Study at a glance

Abstract

This study investigates the neural signatures of consciousness through non-linear dynamics in EEG responses elicited by auditory stimuli. We recorded high-density EEG data from healthy participants during passive listening tasks. Using advanced non-linear analysis methods—such as entropy measures, recurrence quantification analysis, and fractal dimension estimation—we quantified complexity in EEG signals across conscious and altered consciousness states. Results show that conscious auditory processing is characterized by higher entropy and more complex recurrence patterns than non-conscious or reduced-consciousness states. These features reliably discriminated between wakeful and subdued neural conditions, achieving classification accuracy exceeding 85%. Our findings suggest that non-linear EEG metrics can serve as objective markers of consciousness, beyond traditional frequency based analyses. This work paves the way for improved assessment tools in clinical and neuroscientific settings by highlighting how dynamic complexity in neural activity underpins auditory awareness.