Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification

Deep neural networks (DNNs) perform very well when trained on a large amount of data [1], but large SSVEP datasets are not commonly available for open use. Our way to overcome this problem was to employ data augmentation and transfer learning techniques to train the DNNs, as both are known to improve the performances of DNNs on smaller datasets [2]. We started with an open SSVEP dataset [3], which we consider to be large in comparison with other open databases. The electroencephalography (EEG) signals where transformed into images, specifically spectrograms, using the shorttime Fourier transform (STFT). By doing so, we take advantage of the ability of convolutional DNN in classifying images, which is well documented [1]. The neural network used in this study [4] is a DCNN based on the original VGG [5].