  In recent years, neuroscientists have been interested to the development of
brain-computer interface (BCI) devices. Patients with motor disorders may
benefit from BCIs as a means of communication and for the restoration of motor
functions. Electroencephalography (EEG) is one of most used for evaluating the
neuronal activity. In many computer vision applications, deep neural networks
(DNN) show significant advantages. Towards to ultimate usage of DNN, we present
here a shallow neural network that uses mainly two convolutional neural network
(CNN) layers, with relatively few parameters and fast to learn
spectral-temporal features from EEG. We compared this models to three other
neural network models with different depths applied to a mental arithmetic task
using eye-closed state adapted for patients suffering from motor disorders and
a decline in visual functions. Experimental results showed that the shallow CNN
model outperformed all the other models and achieved the highest classification
accuracy of 90.68%. It's also more robust to deal with cross-subject
classification issues: only 3% standard deviation of accuracy instead of 15.6%
from conventional method.
