Title: EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG Show Chinese title

Title: EEG2TEXT-CN：基于大型语言模型和对比学习的开放词汇汉语脑电图-文本对齐的探索性研究

Abstract: 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. Show Chinese abstract

Abstract: 我们提出了EEG2TEXT-CN，据我们所知，这是最早的针对中文的开放式词汇脑电图（EEG）到文本生成框架之一。我们的架构基于生物接地的EEG编码器（NICE-EEG）和紧凑型预训练语言模型（MiniLM），通过掩码预训练和对比学习将多通道脑电信号与自然语言表示对齐。 使用ChineseEEG数据集的一个子集，其中每个句子包含大约十个与256 Hz记录的128通道EEG对齐的汉字，我们将EEG分割为每字符嵌入，并在零样本设置下预测完整句子。解码器使用教师强制和填充掩码进行训练以适应可变长度序列。 在超过1,500个训练验证句子和300个保留测试样本上的评估显示了有希望的词典对齐，最佳BLEU-1得分为6.38％。虽然句法流畅性仍然是一个挑战，但我们的研究结果证明了从EEG进行非音素、跨模态语言解码的可行性。这项工作开启了多语言大脑到文本研究的新方向，并为未来的中文认知语言接口奠定了基础。