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PGAP: Purity-Guided Active Prompting for EEG Decoding With LLMs [Research Frontier]
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DOI：10.1109/mci.2026.3665159.png)
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Abstract
En 中文
Large language models (LLMs) have demonstrated impressive in-context learning capabilities, particularly in few-shot prompting. However, most studies have focused on natural language processing and computer vision, applications to complex physiological signals such as electroencephalogram (EEG) decoding remain largely unexplored. This paper proposes purity-guided active prompting (PGAP), which integrates domain-specific EEG feature extraction with an active learning based demonstration selection strategy. PGAP identifies a compact set of prototypical examples that effectively activate the few-shot capabilities of LLMs without gradient-based parameter updates, and is compatible with both open-source and closed-source LLMs. Unlike the conventional framework that involves random demonstration selection on a per-instance basis for each test sample, PGAP performs a one-time identification of highly representative EEG samples from the sample pool, significantly reducing computational overhead. Extensive experiments on seven EEG datasets and three paradigms demonstrated that PGAP consistently outperformed five existing sample selection baselines, achieving superior accuracy and robustness. Furthermore, the performance improved with more powerful LLMs (e.g., DeepSeek-V3, GPT-4.1), and sometimes even surpassed state-of-the-art supervised models.
Keywords:
Electroencephalography
Brain-computer interfaces
Support vector machines
Large language models
Context awareness
Decoding
Few shot learning
Natural language processing
Computer vision
Journal
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11.2
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606
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3.1K
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