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Updated: Jan 18, 2026

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EEG-CLIP: learning EEG representations from natural language descriptions.

Tidiane Camaret Ndir1,2, Robin T Schirrmeister1,2, Tonio Ball2,3

  • 1Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.

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Summary
This summary is machine-generated.

This study introduces EEG-CLIP, a new deep learning method for electroencephalogram (EEG) analysis. EEG-CLIP aligns EEG data with text reports, enabling versatile decoding for various tasks with less data.

Keywords:
clinical text processingcontrastive learningelectroencephalogram (EEG)multimodal representationneural time seriestransfer learningzero-shot classification

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Medical Informatics

Background:

  • Current deep learning models for electroencephalogram (EEG) decoding are typically trained for single, specific tasks like pathology or age identification.
  • A more generalized approach involves training models to associate EEG recordings with their corresponding clinical text descriptions, inspired by image-caption matching in computer vision.
  • This task-agnostic strategy facilitates zero-shot decoding using textual prompts.

Purpose of the Study:

  • To develop a contrastive learning framework, named EEG-CLIP, for aligning electroencephalogram (EEG) time series with clinical text descriptions in a shared embedding space.
  • To investigate the potential of EEG-CLIP for versatile EEG decoding across various few-shot and zero-shot learning scenarios.
  • To establish a method for learning general EEG representations that support diverse decoding tasks.

Main Methods:

  • Developed the EEG-CLIP framework, a contrastive learning model designed to map EEG time series and associated clinical text descriptions into a unified embedding space.
  • Evaluated the model's performance in multiple few-shot and zero-shot decoding tasks.
  • Utilized a dataset comprising clinical EEG recordings and their corresponding textual medical reports.

Main Results:

  • EEG-CLIP successfully achieved a non-trivial alignment between text descriptions and EEG representations.
  • The model demonstrated effectiveness in versatile EEG decoding tasks, particularly in few-shot and zero-shot settings.
  • The learned general EEG representations showed promise for simplifying the analysis of diverse decoding questions.

Conclusions:

  • The proposed EEG-CLIP framework offers a promising approach for learning general-purpose EEG representations.
  • This method can significantly enhance EEG analysis by enabling zero-shot decoding and improving the efficiency of training task-specific models with limited data.
  • The availability of the code facilitates reproducibility and further research in generalizable EEG decoding.