  Motor imagery (MI) classification based on electroencephalogram (EEG) is a
widely-used technique in non-invasive brain-computer interface (BCI) systems.
Since EEG recordings suffer from heterogeneity across subjects and labeled data
insufficiency, designing a classifier that performs the MI independently from
the subject with limited labeled samples would be desirable. To overcome these
limitations, we propose a novel subject-independent semi-supervised deep
architecture (SSDA). The proposed SSDA consists of two parts: an unsupervised
and a supervised element. The training set contains both labeled and unlabeled
data samples from multiple subjects. First, the unsupervised part, known as the
columnar spatiotemporal auto-encoder (CST-AE), extracts latent features from
all the training samples by maximizing the similarity between the original and
reconstructed data. A dimensional scaling approach is employed to reduce the
dimensionality of the representations while preserving their discriminability.
Second, a supervised part learns a classifier based on the labeled training
samples using the latent features acquired in the unsupervised part. Moreover,
we employ center loss in the supervised part to minimize the embedding space
distance of each point in a class to its center. The model optimizes both parts
of the network in an end-to-end fashion. The performance of the proposed SSDA
is evaluated on test subjects who were not seen by the model during the
training phase. To assess the performance, we use two benchmark EEG-based MI
task datasets. The results demonstrate that SSDA outperforms state-of-the-art
methods and that a small number of labeled training samples can be sufficient
for strong classification performance.
