无提示脑电想象说话实现97.93%身份识别准确率
Cueless EEG imagined speech for subject identification: dataset and benchmarks
- 用户在无外界提示下想象指定词汇，采集脑电信号
- 跨会话验证下达到97.93%的身份识别准确率
- 适合开发高安全性的脑机接口身份认证系统
脑电图（EEG）信号已成为生物特征识别的有前景模态。以往研究多依赖视觉或听觉提示来诱发想象说话，而本研究提出一种无提示的脑电想象说话范式：受试者在无外部提示下，从预定义词汇表中自然选择并想象发音。数据集包含11名受试者、5次会话，共4,350余次试验。我们评估了多种分类方法，包括支持向量机（SVM）、XGBoost等传统机器学习，以及专为EEG设计的深度学习模型如EEG Conformer和Shallow ConvNet。采用会话级留出验证策略以确保评估可靠性并避免数据泄露。实验结果表明，该方法在跨会话验证下取得高达97.93%的分类准确率，凸显无提示脑电范式在真实场景脑机接口中的安全与可靠潜力。
原文摘要 · Abstract (English)
Electroencephalogram (EEG) signals have emerged as a promising modality for biometric identification. While previous studies have explored the use of imagined speech with semantically meaningful words for subject identification, most have relied on additional visual or auditory cues. In this study, we introduce a cueless EEG-based imagined speech paradigm, where subjects imagine the pronunciation of semantically meaningful words without any external cues. This innovative approach addresses the limitations of prior methods by requiring subjects to select and imagine words from a predefined list naturally. The dataset comprises over 4,350 trials from 11 subjects across five sessions. We assess a variety of classification methods, including traditional machine learning techniques such as Support Vector Machines (SVM) and XGBoost, as well as time-series foundation models and deep learning architectures specifically designed for EEG classification, such as EEG Conformer and Shallow ConvNet. A session-based hold-out validation strategy was employed to ensure reliable evaluation and prevent data leakage. Our results demonstrate outstanding classification accuracy, reaching 97.93%. These findings highlight the potential of cueless EEG paradigms for secure and reliable subject identification in real-world applications, such as brain-computer interfaces (BCIs).
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