用脑电波生成中文，首次实现开放词汇的跨模态解码。
EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG
- 基于脑电编码器与小型语言模型，通过对比学习对齐脑电与文本。
- 零样本下生成中文句子，最高BLEU-1达6.38%，验证可行性。
- 适合关注中文脑机接口、多语言脑电解码的研究者。
我们提出EEG2TEXT-CN，据我们所知是首个针对中文的开放词汇脑电到文本生成框架。该框架基于生物可解释的脑电编码器（NICE-EEG）和轻量级预训练语言模型（MiniLM），通过掩码预训练与对比学习，将多通道脑电信号与自然语言表示对齐。利用中国脑电数据集（ChineseEEG）子集，每句约含十个汉字，对应128通道、采样率256 Hz的脑电数据，我们将脑电信号按字分段为嵌入向量，并在零样本设置下预测完整句子。解码器采用教师强制与填充掩码训练，以处理变长序列。在超过1,500条训练-验证句和300条保留测试样本上评估，结果显示具有一定的词汇对齐能力，最佳BLEU-1得分为6.38%。尽管句法流畅性仍存挑战，但本研究证明了非语音类跨模态语言从脑电中解码的可行性。这项工作为多语言脑机文本研究开辟新方向，并为未来中文认知-语言接口奠定基础。
原文摘要 · Abstract (English)
We propose EEG2TEXT-CN, which, to the best of our knowledge, represents one of the earliest open-vocabulary EEG-to-text generation frameworks tailored for Chinese. Built on a biologically grounded EEG encoder (NICE-EEG) and a compact pretrained language model (MiniLM), our architecture aligns multichannel brain signals with natural language representations via masked pretraining and contrastive learning. Using a subset of the ChineseEEG dataset, where each sentence contains approximately ten Chinese characters aligned with 128-channel EEG recorded at 256 Hz, we segment EEG into per-character embeddings and predict full sentences in a zero-shot setting. The decoder is trained with teacher forcing and padding masks to accommodate variable-length sequences. Evaluation on over 1,500 training-validation sentences and 300 held-out test samples shows promising lexical alignment, with a best BLEU-1 score of 6.38\%. While syntactic fluency remains a challenge, our findings demonstrate the feasibility of non-phonetic, cross-modal language decoding from EEG. This work opens a new direction in multilingual brain-to-text research and lays the foundation for future cognitive-language interfaces in Chinese.
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