  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.
