Brain-computer interface (BCI) is the technology that enables the communication between humans and devices by reflecting status and intentions of humans. When conducting imagined speech, the users imagine the pronunciation as if actually speaking. In the case of decoding imagined speech-based EEG signals, complex task can be conducted more intuitively, but decoding performance is lower than that of other BCI paradigms. We modified our previous model for decoding imagined speech-based EEG signals. Ten subjects participated in the experiment. The average accuracy of our proposed method was 0.5648 for classifying four words. In other words, our proposed method has significant strength in learning local features. Hence, we demonstrated the feasibility of decoding imagined speech-based EEG signals with robust performance.

翻译：脑计算机界面( BCI) 是能够通过反映人类状态和意图而使人与装置之间通信的技术。 当进行想象式演讲时, 用户将发音想象成实际说话。 在解码以语言为基础的模拟 EEG 信号的情况下, 任务比较复杂, 但解码性能比其他 BCI 模式要低。 我们修改了我们先前用于解码以语言为基础的模拟 EEG 信号的模式。 参加实验的有十个主题。 我们拟议方法的平均精确度是 0. 5648 用于对四个字进行分类。 换句话说, 我们提议的方法在学习本地特征方面有很大的优势。 因此, 我们展示了将以语言为基础的信号进行解码的可行性。