  Recently, various deep neural networks have been applied to classify
electroencephalogram (EEG) signal. EEG is a brain signal that can be acquired
in a non-invasive way and has a high temporal resolution. It can be used to
decode the intention of users. As the EEG signal has a high dimension of
feature space, appropriate feature extraction methods are needed to improve
classification performance. In this study, we obtained spatio-temporal feature
representation and classified them with the combined convolutional neural
networks (CNN)-gated recurrent unit (GRU) model. To this end, we obtained
covariance matrices in each different temporal band and then concatenated them
on the temporal axis to obtain a final spatio-temporal feature representation.
In the classification model, CNN is responsible for spatial feature extraction
and GRU is responsible for temporal feature extraction. Classification
performance was improved by distinguishing spatial data processing and temporal
data processing. The average accuracy of the proposed model was 77.70% for the
BCI competition IV_2a data set. The proposed method outperformed all other
methods compared as a baseline method.
