Bi-dimensional representation of EEGs for BCI classification using CNN architectures

An important challenge when designing Brain Computer Interfaces (BCI) is to create a pipeline (signal conditioning, feature extraction and classification) requiring minimal parameter adjustments for each subject and each run. On the other hand, Convolutional Neural Networks (CNN) have shown outstanding to automatically extract features from images, which may help when distribution of input data is unknown and irregular. To obtain full benefits of a CNN, we propose two meaningful image representations built from multichannel EEG signals. Images are built from spectrograms and scalograms. We evaluated two kinds of classifiers: one based on a CNN-2D and the other built using a CNN-2D combined with a LSTM. Our experiments showed that this pipeline allows to use the same channels and architectures for all subjects, getting competitive accuracy using different datasets: 71.3 ± 11.9% for BCI IV-2a (four classes); 80.7 ± 11.8 % for BCI IV-2a (two classes); 73.8 ± 12.1% for BCI IV-2b; 83.6 ± 1.0% for BCI II-III and 82.10% ± 6.9% for a private database based on mental calculation.