  Classification of EEG-based motor imagery (MI) is a crucial non-invasive
application in brain-computer interface (BCI) research. This paper proposes a
novel convolutional neural network (CNN) architecture for accurate and robust
EEG-based MI classification that outperforms the state-of-the-art methods. The
proposed CNN model, namely EEG-Inception, is built on the backbone of the
Inception-Time network, which showed to be highly efficient and accurate for
time-series classification. Also, the proposed network is an end-to-end
classification, as it takes the raw EEG signals as the input and does not
require complex EEG signal-preprocessing. Furthermore, this paper proposes a
novel data augmentation method for EEG signals to enhance the accuracy, at
least by 3%, and reduce overfitting with limited BCI datasets. The proposed
model outperforms all the state-of-the-art methods by achieving the average
accuracy of 88.4% and 88.6% on the 2008 BCI Competition IV 2a (four-classes)
and 2b datasets (binary-classes), respectively. Furthermore, it takes less than
0.025 seconds to test a sample suitable for real-time processing. Moreover, the
classification standard deviation for nine different subjects achieves the
lowest value of 5.5 for the 2b dataset and 7.1 for the 2a dataset, which
validates that the proposed method is highly robust. From the experiment
results, it can be inferred that the EEG-Inception network exhibits a strong
potential as a subject-independent classifier for EEG-based MI tasks.
