  Electroencephalogram (EEG) has been one of the common neuromonitoring
modalities for real-world brain-computer interfaces (BCIs) because of its
non-invasiveness, low cost, and high temporal resolution. Recently,
light-weight and portable EEG wearable devices based on low-density montages
have increased the convenience and usability of BCI applications. However, loss
of EEG decoding performance is often inevitable due to reduced number of
electrodes and coverage of scalp regions of a low-density EEG montage. To
address this issue, we introduce knowledge distillation (KD), a learning
mechanism developed for transferring knowledge/information between neural
network models, to enhance the performance of low-density EEG decoding. Our
framework includes a newly proposed similarity-keeping (SK) teacher-student KD
scheme that encourages a low-density EEG student model to acquire the
inter-sample similarity as in a pre-trained teacher model trained on
high-density EEG data. The experimental results validate that our SK-KD
framework consistently improves motor-imagery EEG decoding accuracy when number
of electrodes deceases for the input EEG data. For both common low-density
headphone-like and headband-like montages, our method outperforms
state-of-the-art KD methods across various EEG decoding model architectures. As
the first KD scheme developed for enhancing EEG decoding, we foresee the
proposed SK-KD framework to facilitate the practicality of low-density
EEG-based BCI in real-world applications.
