TOINet: Transfer Learning from Overt Speech- to Imagined Speech-Based EEG Signals with Convolutional Autoencoder

Brain-computer interface (BCI) enables the communication between humans and devices by reflecting humans' intentions and status. Endogenous BCI is the imagined-based BCI and it has the advantage that the fatigue level of the body, especially the eyes, is relatively low and no additional equipment for offering stimulation is required. When conducting imagined speech, one of the endogenous BCI paradigms, the users imagine the pronunciation as if actually speaking. In contrast, overt speech is that the users directly pronounce the words. We proposed the transfer learning-based method from overt speech- to imagined speech-based electroencephalogram (EEG) signals (TOINet). The proposed method utilizes an encoder to extract the feature vector of imagined speech from EEG signals, which is subsequently reconstructed into overt speech signals using the decoder. Through this process, the model can identify the significant and common features present in EEG signals for both overt and imagined speech, facilitating the classification of EEG signals associated with imagined speech. Eight subjects participated in the experiment. The average accuracy of the TOINet was 0.4841 for classifying four words and the EEG features of overt speech improved the performance by 0.0742. Hence, we demonstrated that EEG features of overt speech could improve the decoding performance of imagined speech.