用大模型从脑电数据中提取通用语义特征，提升跨人识别能力。
LLMs Help Alleviate the Cross-Subject Variability in Brain Signal and Language Alignment
- 用大语言模型做去噪器，从嘈杂脑电中提取共性语义
- 零样本测试下对未见受试者仍能准确解码语义
- 适合想提升脑机接口泛化能力的研究者
从脑电（EEG）信号解码人类活动是长期研究课题。尽管近年研究重心已转向跨被试分析，但很少探讨模型对未见过被试的零样本预测能力。本研究旨在验证深度学习方法能否捕捉到隐藏在人类EEG信号中的跨被试语义信息。这一发现对脑机接口（BCI）至关重要：一方面体现模型对个体时序偏差的鲁棒性，另一方面显著提升下游任务的泛化能力。我们采用大语言模型（LLMs）作为去噪代理，从噪声EEG信号中提取跨被试语义特征。实验结果及消融研究均表明，LLMs在从噪声EEG数据中解码跨被试语义信息方面起关键作用。希望本研究能推动BCI发展，并助力学术界与产业界将脑电信号应用于更广泛场景。
原文摘要 · Abstract (English)
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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