用说话语音训练模型，直接用于默想语音识别，减少训练负担。
Transfer Learning for Covert Speech Classification Using EEG Hilbert Envelope and Temporal Fine Structure
- 从公开语音数据迁移训练，避免受试者反复默念单词
- 默想语音分类准确率达79.82%，接近公开语音的86.44%
- 适合需要快速部署、减少用户疲劳的脑机接口场景
脑机接口可从神经活动解码想象中的言语。但现有系统通常需要大量训练，参与者反复默念词语，易产生心理疲劳，且难以准确识别词语起始点，尤其在连续默念时。本文提出将公开语音数据训练的分类器迁移至默想语音分类任务。采用基于希尔伯特包络与时间精细结构的脑电（EEG）特征，构建双向长短期记忆（BiLSTM）模型。该方法显著降低训练负担，在公开语音任务上达86.44%准确率，默想语音任务达79.82%，实现当前最优性能。
原文摘要 · Abstract (English)
Brain-Computer Interfaces (BCIs) can decode imagined speech from neural activity. However, these systems typically require extensive training sessions where participants imaginedly repeat words, leading to mental fatigue and difficulties identifying the onset of words, especially when imagining sequences of words. This paper addresses these challenges by transferring a classifier trained in overt speech data to covert speech classification. We used electroencephalogram (EEG) features derived from the Hilbert envelope and temporal fine structure, and used them to train a bidirectional long-short-term memory (BiLSTM) model for classification. Our method reduces the burden of extensive training and achieves state-of-the-art classification accuracy: 86.44% for overt speech and 79.82% for covert speech using the overt speech classifier.
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