The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs). With the swift advancement of large language models, such as ChatGPT, the need to bridge the gap between the brain and languages becomes increasingly pressing. Current methods, however, require eye-tracking fixations or event markers to segment brain dynamics into word-level features, which can restrict the practical application of these systems. To tackle these issues, we introduce a novel framework, DeWave, that integrates discrete encoding sequences into open-vocabulary EEG-to-text translation tasks. DeWave uses a quantized variational encoder to derive discrete codex encoding and align it with pre-trained language models. This discrete codex representation brings forth two advantages: 1) it realizes translation on raw waves without marker by introducing text-EEG contrastive alignment training, and 2) it alleviates the interference caused by individual differences in EEG waves through an invariant discrete codex with or without markers. Our model surpasses the previous baseline (40.1 and 31.7) by 3.06% and 6.34%, respectively, achieving 41.35 BLEU-1 and 33.71 Rouge-F on the ZuCo Dataset. This work is the first to facilitate the translation of entire EEG signal periods without word-level order markers (e.g., eye fixations), scoring 20.5 BLEU-1 and 29.5 Rouge-1 on the ZuCo Dataset.

翻译：将脑动态翻译为自然语言对脑机接口（BCIs）至关重要。随着ChatGPT等大型语言模型的快速发展，弥合大脑与语言之间鸿沟的需求日益迫切。然而，现有方法需要依赖眼动追踪固定点或事件标记将脑动态分割为词级特征，这限制了这些系统的实际应用。为解决这些问题，我们提出一种新颖框架DeWave，将离散编码序列集成到开放词汇的脑电图到文本翻译任务中。DeWave利用量化变分编码器提取离散编码序列，并将其与预训练语言模型对齐。这种离散编码表示带来两大优势：1）通过引入文本-EEG对比对齐训练，实现了无标记的原始脑波翻译；2）通过具有或不具有标记的恒定离散编码，缓解了脑电波个体差异造成的干扰。我们的模型在ZuCo数据集上分别达到41.35 BLEU-1和33.71 Rouge-F，相较此前基线（40.1和31.7）提升3.06%和6.34%。本研究首次实现无需词级顺序标记（如眼动固定点）的完整脑电波周期翻译，在ZuCo数据集上取得20.5 BLEU-1和29.5 Rouge-1的成绩。