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

DAL: Feature Learning from Overt Speech to Decode Imagined Speech-based EEG Signals with Convolutional Autoencoder