Imagined speech is a highly promising paradigm due to its intuitive application and multiclass scalability in the field of brain-computer interfaces. However, optimal feature extraction and classifiers have not yet been established. Furthermore, retraining still requires a large number of trials when new classes are added. The aim of this study is (i) to increase the classification performance for imagined speech and (ii) to apply a new class using a pretrained classifier with a small number of trials. We propose a novel framework based on deep metric learning that learns the distance by comparing the similarity between samples. We also applied the instantaneous frequency and spectral entropy used for speech signals to electroencephalography signals during imagined speech. The method was evaluated on two public datasets (6-class Coretto DB and 5-class BCI Competition DB). We achieved a 6-class accuracy of 45.00 ± 3.13% and a 5-class accuracy of 48.10 ± 3.68% using the proposed method, which significantly outperformed state-of-the-art methods. Additionally, we verified that the new class could be detected through incremental learning with a small number of trials. As a result, the average accuracy is 44.50 ± 0.26% for Coretto DB and 47.12 ± 0.27% for BCI Competition DB, which shows similar accuracy to baseline accuracy without incremental learning. Our results have shown that the accuracy can be greatly improved even with a small number of trials by selecting appropriate features from imagined speech. The proposed framework could be directly used to help construct an extensible intuitive communication system based on brain-computer interfaces.

中文翻译：

---

基于深度度量学习解码想象语音用于直观的 BCI 通信

由于其在脑机接口领域的直观应用和多类可扩展性，想象语音是一种非常有前途的范式。然而，最佳特征提取和分类器尚未建立。此外，当添加新类时，再训练仍然需要大量的试验。本研究的目的是 (i) 提高想象语音的分类性能，以及 (ii) 使用经过少量试验的预训练分类器应用新类别。我们提出了一种基于深度度量学习的新框架，该框架通过比较样本之间的相似性来学习距离。我们还将用于语音信号的瞬时频率和频谱熵应用于想象语音期间的脑电图信号。该方法在两个公共数据集（6 级 Coretto DB 和 5 级 BCI 竞赛数据库）上进行了评估。我们使用所提出的方法实现了 45.00 ± 3.13% 的 6 级精度和 48.10 ± 3.68% 的 5 级精度，这显着优于最先进的方法。此外，我们验证了可以通过少量试验通过增量学习检测到新类。因此，Coretto DB 的平均准确度为 44.50 ± 0.26%，BCI Competition DB 的平均准确度为 47.12 ± 0.27%，在没有增量学习的情况下显示出与基线准确度相似的准确度。我们的结果表明，通过从想象的语音中选择适当的特征，即使进行少量试验，准确度也可以大大提高。