AVMoments-EEG: A Large-Scale EEG Dataset of Naturalistic Audiovisual Event Perception

Authors

Tang, S., Lu, Z., Li, L., Li, D.

Abstract

Understanding how the human brain processes dynamic audiovisual events requires datasets that combine naturalistic stimulation, high temporal resolution, and systematic experimental manipulation of sensory information. Here, we introduce AVMoments-EEG, the first large-scale, high-temporal-resolution human EEG dataset designed to characterize neural responses to naturalistic audiovisual events. Across 10 participants, we recorded 64-channel scalp EEG during free viewing of 3-second naturalistic video clips derived from the Audiovisual Moments in Time dataset. The stimulus set comprised 896 training videos (56 videos in each of 16 event types), each presented three times, and 64 held-out test videos (4 videos in each of 16 types), each presented 40 times in each of three conditions: (1) intact audiovisual input, (2) intact visual input with scrambled audio, and (3) intact auditory input with scrambled visual input. This design yielded 10,368 trials per participant and combines broad stimulus coverage with highly repeated test stimuli. AVMoments-EEG enables temporally resolved investigation of naturalistic event processing and modality-specific audiovisual contributions, while providing a benchmark for video-to-EEG encoding, EEG-based decoding, and brain-model alignment. The dataset offers a resource for studying dynamic event perception, multisensory processing, and the temporal correspondence between human neural responses and artificial neural network representations.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 24 Sep 2026.

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