Decoding imagined speech from human brain signals is a challenging and important issue that may enable human communication via brain signals. While imagined speech can be the paradigm for silent communication via brain signals, it is always hard to collect enough stable data to train the decoding model. Meanwhile, spoken speech data is relatively easy and to obtain, implying the significance of utilizing spoken speech brain signals to decode imagined speech. In this paper, we performed a preliminary analysis to find out whether if it would be possible to utilize spoken speech electroencephalography data to decode imagined speech, by simply applying the pre-trained model trained with spoken speech brain signals to decode imagined speech. While the classification performance of imagined speech data solely used to train and validation was 30.5 %, the transferred performance of spoken speech based classifier to imagined speech data displayed average accuracy of 26.8 % which did not have statistically significant difference compared to the imagined speech based classifier (p = 0.0983, chi-square = 4.64). For more comprehensive analysis, we compared the result with the visual imagery dataset, which would naturally be less related to spoken speech compared to the imagined speech. As a result, visual imagery have shown solely trained performance of 31.8 % and transferred performance of 26.3 % which had shown statistically significant difference between each other (p = 0.022, chi-square = 7.64). Our results imply the potential of applying spoken speech to decode imagined speech, as well as their underlying common features.

翻译：从人脑信号中解码想象语音是一项具有挑战性的重要课题，有望通过脑信号实现人类通信。虽然想象语音可作为通过脑信号进行无声通信的范式，但始终难以收集足够稳定的数据来训练解码模型。相比之下，口语语音数据较易获取，这突显了利用口语语音脑信号解码想象语音的重要意义。本文通过初步分析，探索直接应用基于口语语音脑信号的预训练模型解码想象语音的可行性。结果显示，仅使用想象语音数据训练和验证的分类准确率为30.5%，而基于口语语音的分类器迁移至想象语音数据的平均准确率为26.8%，与基于想象语音的分类器无统计学显著差异（p=0.0983，卡方值=4.64）。为进行更全面的分析，我们将其与视觉想象数据集进行比较——后者与口语语音的关联性自然弱于想象语音。结果显示，视觉想象任务的单独训练准确率为31.8%，迁移性能为26.3%，两者存在统计学显著差异（p=0.022，卡方值=7.64）。本研究结果表明，利用口语语音解码想象语音具有潜在可行性，且两类信号间存在潜在共性特征。