  Recently, practical brain-computer interface is actively carried out,
especially, in an ambulatory environment. However, the electroencephalography
(EEG) signals are distorted by movement artifacts and electromyography signals
when users are moving, which make hard to recognize human intention. In
addition, as hardware issues are also challenging, ear-EEG has been developed
for practical brain-computer interface and has been widely used. In this paper,
we proposed ensemble-based convolutional neural networks in ambulatory
environment and analyzed the visual event-related potential responses in scalp-
and ear-EEG in terms of statistical analysis and brain-computer interface
performance. The brain-computer interface performance deteriorated as 3-14%
when walking fast at 1.6 m/s. The proposed methods showed 0.728 in average of
the area under the curve. The proposed method shows robust to the ambulatory
environment and imbalanced data as well.
