arXiv:2606.00815v1 Announce Type: new 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
The proliferation of EEG foundation models necessitates standardized evaluation to enable coherent progress and comparison, addressing fragmentation in research and deployment.
A standardized benchmark for EEG Foundation Models will accelerate the development and reliability of brain-computer interfaces, impacting fields from healthcare to human-computer interaction.
The fragmented evaluation of EEG models will begin to converge around a unified benchmark, fostering more direct comparisons and rapid advancements in the field.
- · AI researchers
- · BCI developers
- · Healthcare providers
- · Neurology
- · Disparate research silos
- · Inefficient model development
Researchers can more effectively compare and improve EEG foundation models using a common set of tasks and metrics.
Improved EEG foundation models lead to more robust and accurate brain-computer interfaces and neuroprosthetics.
Enhanced BCI technology could enable new forms of human-computer interaction and therapeutic interventions for neurological conditions.
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