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This paper applies TopK Sparse Autoencoders to three EEG foundation models (SleepFM, REVE, LaBraM) to extract interpretable feature dictionaries and introduces a framework for concept steering, revealing representational failures and clinical entanglements.
Researchers propose Brain-CLIPLM, a two-stage EEG-to-text decoding framework using contrastive learning for semantic anchor extraction and a retrieval-grounded LLM with Chain-of-Thought reasoning, achieving 67.55% top-5 sentence retrieval accuracy and suggesting EEG-to-text decoding should focus on recovering compressed semantic content rather than full sentence reconstruction.