Researchers have developed a new benchmark called BrainBench to evaluate the ability of large language models (LLMs) to understand electroencephalography (EEG) recordings. The benchmark consists of four subsets and covers various tasks such as sleep assessment and neurocognitive assessment. It assesses LLMs' performance through numerical, categorical, and semantic validation. The authors evaluated several representative LLMs using BrainBench and found significant variations in their performance across different models, subsets, and difficulty levels. --- Why it matters: This matters to AI engineers because it provides a standardized testbed for evaluating the EEG understanding capabilities of large language models. This can help advance the development of more accurate and reliable EEG analysis systems. Source: https://arxiv.org/abs/2608.04156