Correlation Between Pre-training Dataset Diversity and Noise Robustness in EEG Foundation Models on OmniEEG-Bench

Abstract

Abstract: Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions. EEG foundation models are emerging, but evaluation remains fragmented due to heterogeneous datasets and nconsistent task protocols. Here, we introduce OmniEEG-Bench, a unified benchmark and downstream task roadmap for EEG foundation models (FMs). It organizes evaluation of EEG FMs into six task families spanning (i) signal reliability, (ii) biometrics and disease, (iii) consciousness and state, (iv) cognition and emotion, (v) naturalistic stimul

Research Question

What is the correlation between pre-training dataset diversity and robustness to signal noise in EEG foundation models evaluated on the signal reliability task family of OmniEEG-Bench?

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