  Brain-computer interfaces (BCIs) enable communication between humans and
machines by translating brain activity into control commands.
Electroencephalography (EEG) signals are one of the most used brain signals in
non-invasive BCI applications but are often contaminated with noise. Therefore,
it is possible that meaningful patterns for classifying EEG signals are deeply
hidden. State-of-the-art deep-learning algorithms are successful in learning
hidden, meaningful patterns. However, the quality and the quantity of the
presented inputs is pivotal. Here, we propose a novel feature extraction method
called anchored Short Time Fourier Transform (anchored-STFT), which is an
advanced version of STFT, as it minimizes the trade-off between temporal and
spectral resolution presented by STFT. In addition, we propose a novel
augmentation method, called gradient norm adversarial augmentation (GNAA). GNAA
is not only an augmentation method but is also used to harness adversarial
inputs in EEG data, which not only improves the classification accuracy but
also enhances the robustness of the classifier. In addition, we also propose a
new CNN architecture, namely Skip-Net, for the classification of EEG signals.
The proposed pipeline outperforms all state-of-the-art methods and yields an
average classification accuracy of 90.7 % and 89.54 % on BCI competition II
dataset III and BCI competition IV dataset 2b, respectively.
