LLMs Help Alleviate the Cross-Subject Variability in Brain Signal and Language Alignment

• URL: http://arxiv.org/abs/2501.02621v1
• Date: Sun, 05 Jan 2025 18:29:39 GMT
• Title: LLMs Help Alleviate the Cross-Subject Variability in Brain Signal and Language Alignment
• Authors: Yifei Liu, Hengwei Ye, Shuhang Li,
• Abstract summary: This research aims to investigate whether deep learning methods can capture subject-independent semantic information inherent in human EEG signals. We employ Large Language Models (LLMs) as denoising agents to extract subject-independent semantic features from noisy EEG signals.
• Score: 1.182997366332405
• License:
• Abstract: Decoding human activity from EEG signals has long been a popular research topic. While recent studies have increasingly shifted focus from single-subject to cross-subject analysis, few have explored the model's ability to perform zero-shot predictions on EEG signals from previously unseen subjects. This research aims to investigate whether deep learning methods can capture subject-independent semantic information inherent in human EEG signals. Such insights are crucial for Brain-Computer Interfaces (BCI) because, on one hand, they demonstrate the model's robustness against subject-specific temporal biases, and on the other, they significantly enhance the generalizability of downstream tasks. We employ Large Language Models (LLMs) as denoising agents to extract subject-independent semantic features from noisy EEG signals. Experimental results, including ablation studies, highlight the pivotal role of LLMs in decoding subject-independent semantic information from noisy EEG data. We hope our findings will contribute to advancing BCI research and assist both academia and industry in applying EEG signals to a broader range of applications.

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