WaveMind: Towards a Conversational EEG Foundation Model Aligned to Textual and Visual Modalities

• URL: http://arxiv.org/abs/2510.00032v1
• Date: Fri, 26 Sep 2025 06:21:51 GMT
• Title: WaveMind: Towards a Conversational EEG Foundation Model Aligned to Textual and Visual Modalities
• Authors: Ziyi Zeng, Zhenyang Cai, Yixi Cai, Xidong Wang, Junying Chen, Rongsheng Wang, Yipeng Liu, Siqi Cai, Benyou Wang, Zhiguo Zhang, Haizhou Li,
• Abstract summary: EEG signals simultaneously encode both cognitive processes and intrinsic neural states.<n>We map EEG signals and their corresponding modalities into a unified semantic space to achieve generalized interpretation.<n>The resulting model demonstrates robust classification accuracy while supporting flexible, open-ended conversations.
• Score: 55.00677513249723
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electroencephalography (EEG) interpretation using multimodal large language models (MLLMs) offers a novel approach for analyzing brain signals. However, the complex nature of brain activity introduces critical challenges: EEG signals simultaneously encode both cognitive processes and intrinsic neural states, creating a mismatch in EEG paired-data modality that hinders effective cross-modal representation learning. Through a pivot investigation, we uncover complementary relationships between these modalities. Leveraging this insight, we propose mapping EEG signals and their corresponding modalities into a unified semantic space to achieve generalized interpretation. To fully enable conversational capabilities, we further introduce WaveMind-Instruct-338k, the first cross-task EEG dataset for instruction tuning. The resulting model demonstrates robust classification accuracy while supporting flexible, open-ended conversations across four downstream tasks, thereby offering valuable insights for both neuroscience research and the development of general-purpose EEG models.

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