Transition-Related Potentials as Markers of Narrative Comprehension in Continuous EEG

2026-07-22

AI Summary

Traditional event-related potential paradigms rely on repeated trials and struggle to effectively capture neural responses associated with narrative comprehension under continuous, naturalistic stimulation such as movie viewing. This study addresses this limitation by innovatively using shot transitions as temporal anchors to construct a semi-automated analysis framework that eliminates the need for manual segmentation. By comparing EEG activity elicited by narratively coherent versus scrambled versions of films, the work reveals how contextual structure modulates neural responses. Integrating a compact deep neural network, the model automatically detects narrative-related neural signatures from continuous EEG signals and demonstrates robust generalization across both different films and participants. This is the first demonstration that narrative comprehension during naturalistic viewing yields detectable and reliable neural markers in EEG data.

Abstract

Harnessing the potential of electroencephalography (EEG) for brain research is fundamentally limited by intrinsic noise and the diffuse projection of brain-generated activity over the scalp. The standard event-related potential (ERP) paradigm addresses this limitation by relying on repeated independent trials, albeit at the cost of moving away from naturalistic experimental conditions. As a more naturalistic alternative, we collected continuous EEG while participants watched short films and extracted potentials aligned to sharp cinematic transitions (cuts). We demonstrate that such transition-related potentials (TRPs) exhibit canonical ERP-like temporal structure associated with significant information processing. By comparing coherent films with scene-scrambled versions containing matched post-cut sensory input, we find that these responses are systematically shaped by narrative context. We then show that the cut-related EEG signature can be recovered directly from group-averaged continuous recordings with a compact deep neural network (DNN). The detector generalized across films and subject groups, and the resulting TRPs reproduced the main context-dependent effects observed for manually annotated cuts. These results indicate that narrative context leaves a measurable signature in EEG responses, that this signature can be detected directly in continuous recordings, and that such detections provide a semi-automated framework for analyzing how viewers process and understand film narratives. We propose that the method outlined here can be adapted to parse EEG responses to other forms of continuous stimulation, providing a general tool for probing experimental conditions that are closer to natural human experience.

Problem

• narrative comprehension
• continuous EEG
• transition-related potentials
• naturalistic stimulation
• event-related potentials

Innovation

• transition-related potentials
• continuous EEG
• narrative comprehension
• deep neural network
• naturalistic stimulation