AI 中文总结
本研究通过对比神经解码器和神经约束束搜索，将语言模型整合到MEG语音解码中，发现想象语音比听读语音更受益于语言模型，尤其在神经证据较弱时，对想象语音脑机接口具有重要价值。
AI 中文摘要
解码想象语音是脑机接口的一个重要目标，但由于神经反应较弱、信噪比低以及想象语音数据集有限，这一目标仍然具有挑战性。语言模型为文本预测提供了强大的上下文线索，但它们能在多大程度上帮助神经解码，以及它们的贡献在解码感知语音和想象语音时是否有所不同，目前尚不清楚。为了研究这一问题，我们使用了一个配对的听读-想象MEG数据集，并在两个阶段整合语言模型信息。首先，我们训练了一个对比神经解码器，将MEG表征与声学和上下文语言表征对齐，从而提高了听读和想象语音的跨受试者单词解码性能。其次，在推理阶段，我们引入了一个神经约束的束搜索框架，将神经证据与语言模型的下一词概率相结合。我们发现，想象语音解码从语言模型中获益多于听读语音解码。对于想象语音，神经证据与语言模型证据之间的最佳平衡点向语言模型偏移，且相对于仅使用神经证据的解码，其增益更大。总之，这些结果表明，当神经证据较弱时，语言先验最为有用，这使得它们对想象语音脑机接口特别有价值。
英文摘要
Decoding imagined speech is an important goal for brain-computer interfaces but remains challenging due to weak neural responses, low signal-to-noise ratio, and limited imagined-speech datasets. Language models provide strong contextual cues for text prediction, but how much they can help neural decoding and whether their contribution differs for decoding perceived and imagined speech remains unclear. To investigate this, we use a paired listened-imagined MEG dataset and incorporate language-model information at two stages. First, we train a contrastive neural decoder that aligns MEG representations with acoustic and contextual language representations, improving cross-subject word decoding for both listened and imagined speech. Second, at inference, we introduce a neural-constrained beam-search framework that combines neural evidence with language-model next-word probabilities. We find that imagined-speech decoding benefits more from the language model than listened-speech decoding. For Imagined speech, the best-performing balance between neural and language-model evidence shifts toward the language model, and the gain over neural-only decoding is larger. Together, these results suggest that language priors are most useful when neural evidence is weaker, making them particularly valuable for imagined-speech BCIs.
