  In this paper, the deep learning (DL) approach is applied to a joint training
scheme for asynchronous motor imagery-based Brain-Computer Interface (BCI). The
proposed DL approach is a cascade of one-dimensional convolutional neural
networks and fully-connected neural networks (CNN-FC). The focus is mainly on
three types of brain responses: non-imagery EEG (\textit{background EEG}),
(\textit{pure imagery}) EEG, and EEG during the transitional period between
background EEG and pure imagery (\textit{transitional imagery}). The study of
transitional imagery signals should provide greater insight into real-world
scenarios. It may be inferred that pure imagery and transitional EEG are high
and low power EEG imagery, respectively. Moreover, the results from the CNN-FC
are compared to the conventional approach for motor imagery-BCI, namely the
common spatial pattern (CSP) for feature extraction and support vector machine
(SVM) for classification (CSP-SVM). Under a joint training scheme, pure and
transitional imagery are treated as the same class, while background EEG is
another class. Ten-fold cross-validation is used to evaluate whether the joint
training scheme significantly improves the performance task of classifying pure
and transitional imagery signals from background EEG. Using sparse of just a
few electrode channels ($C_{z}$, $C_{3}$ and $C_{4}$), mean accuracy reaches
71.52 % and 70.27 % for CNN-FC and CSP-SVM, respectively. On the other hand,
mean accuracy without the joint training scheme achieve only 62.68 % and 52.41
% for CNN-FC and CSP-SVM, respectively.
