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Brain&#x2013;computer interface (BCI) is the technology that enables the communication between humans and devices by reflecting humans&#x2019; status and intentions. When conducting imagined speech, the users imagine the pronunciation as if actually speaking. In the case of decoding imagined speech&#x2013; based EEG signals, the 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&#x2013;based EEG signals. Ten subjects participated in the experiment. The average accuracy of our proposed method was 0.5648 (&#x00B1;0.0197) for classifying four words. In other words, our proposed method has the significant strength in learning local features. Hence, we demonstrated the feasibility of decoding imagined speech&#x2013;based EEG signals with robust performance.

Decoding High–level Imagined Speech using Attention–based Deep Neural Networks