EEG2MM-Bench: A Unified Benchmark for EEG-to-Multimodal Tasks

EEG-to-multimodal decoding aims to retrieve or reconstruct images, videos, and text from electroencephalography (EEG) signals. However, the transferability of pretrained EEG representations to these tasks remains unclear, while inconsistent evaluation protocols and strong generative priors complicate comparisons. We introduce EEG2MM-Bench, a unified benchmark comprising 11 existing datasets with 468 hours of EEG recordings. We evaluate 17 task-specific architectures and three EEG foundation models across multimodal retrieval and reconstruction under shared data splits and consistent evaluation protocols. Retrieval uses a common alignment and ranking protocol. Reconstruction combines original-system evaluations with unified modality-specific pipelines, comparing EEG encoders under consistent downstream architectures and training procedures with frozen pretrained generators. Gaussian-noise input controls further assess reconstruction sensitivity to EEG replacement. By jointly evaluating stimulus matching and content recovery, EEG2MM-Bench provides a common framework for characterizing the capabilities and limitations of pretrained and task-specific EEG representations across multimodal decoding tasks.