  An alternative pathway for the human brain to communicate with the outside
world is by means of a brain computer interface (BCI). A BCI can decode
electroencephalogram (EEG) signals of brain activities, and then send a command
or an intent to an external interactive device, such as a wheelchair. The
effectiveness of the BCI depends on the performance in decoding the EEG.
Usually, the EEG is contaminated by different kinds of artefacts (e.g.,
electromyogram (EMG), background activity), which leads to a low decoding
performance. A number of filtering methods can be utilized to remove or weaken
the effects of artefacts, but they generally fail when the EEG contains extreme
artefacts. In such cases, the most common approach is to discard the whole data
segment containing extreme artefacts. This causes the fatal drawback that the
BCI cannot output decoding results during that time. In order to solve this
problem, we employ the Lomb-Scargle periodogram to estimate the spectral power
from incomplete EEG (after removing only parts contaminated by artefacts), and
Denoising Autoencoder (DAE) for learning. The proposed method is evaluated with
motor imagery EEG data. The results show that our method can successfully
decode incomplete EEG to good effect.
