  This paper proposed a Multi-Channel Multi-Domain (MCMD) based knowledge
distillation algorithm for sleep staging using single-channel EEG. Both
knowledge from different domains and different channels are learnt in the
proposed algorithm, simultaneously. A multi-channel pre-training and
single-channel fine-tuning scheme is used in the proposed work. The knowledge
from different channels in the source domain is transferred to the
single-channel model in the target domain. A pre-trained teacher-student model
scheme is used to distill knowledge from the multi-channel teacher model to the
single-channel student model combining with output transfer and intermediate
feature transfer in the target domain. The proposed algorithm achieves a
state-of-the-art single-channel sleep staging accuracy of 86.5%, with only 0.6%
deterioration from the state-of-the-art multi-channel model. There is an
improvement of 2% compared to the baseline model. The experimental results show
that knowledge from multiple domains (different datasets) and multiple channels
(e.g. EMG, EOG) could be transferred to single-channel sleep staging.
