  Brain-computer interface (BCI) is one of the tools which enables the
communication between humans and devices by reflecting intention and status of
humans. With the development of artificial intelligence, the interest in
communication between humans and drones using electroencephalogram (EEG) is
increased. Especially, in the case of controlling drone swarms such as
direction or formation, there are many advantages compared with controlling a
drone unit. Imagined speech is one of the endogenous BCI paradigms, which can
identify intentions of users. When conducting imagined speech, the users
imagine the pronunciation as if actually speaking. In contrast, overt speech is
a task in which the users directly pronounce the words. When controlling drone
swarms using imagined speech, complex commands can be delivered more
intuitively, but decoding performance is lower than that of other endogenous
BCI paradigms. We proposed the Deep-autoleaner (DAL) to learn EEG features of
overt speech for imagined speech-based EEG signals classification. To the best
of our knowledge, this study is the first attempt to use EEG features of overt
speech to decode imagined speech-based EEG signals with an autoencoder. A total
of eight subjects participated in the experiment. When classifying four words,
the average accuracy of the DAL was 48.41%. In addition, when comparing the
performance between w/o and w/ EEG features of overt speech, there was a
performance improvement of 7.42% when including EEG features of overt speech.
Hence, we demonstrated that EEG features of overt speech could improve the
decoding performance of imagined speech.
