  Objective: We used deep convolutional neural networks (DCNNs) to classify
electroencephalography (EEG) signals in a steady-state visually evoked
potentials (SSVEP) based single-channel brain-computer interface (BCI), which
does not require calibration on the user.
  Methods: EEG signals were converted to spectrograms and served as input to
train DCNNs using the transfer learning technique. We also modified and applied
a data augmentation method, SpecAugment, generally employed for speech
recognition. Furthermore, for comparison purposes, we classified the SSVEP
dataset using Support-vector machines (SVMs) and Filter Bank canonical
correlation analysis (FBCCA).
  Results: Excluding the evaluated user's data from the fine-tuning process, we
reached 82.2% mean test accuracy and 0.825 mean F1-Score on 35 subjects from an
open dataset, using a small data length (0.5 s), only one electrode (Oz) and
the DCNN with transfer learning, window slicing (WS) and SpecAugment's time
masks.
  Conclusion: The DCNN results surpassed SVM and FBCCA performances, using a
single electrode and a small data length. Transfer learning provided minimal
accuracy change, but made training faster. SpecAugment created a small
performance improvement and was successfully combined with WS, yielding higher
accuracies.
  Significance: We present a new methodology to solve the problem of SSVEP
classification using DCNNs. We also modified a speech recognition data
augmentation technique and applied it to the context of BCIs. The presented
methodology surpassed performances obtained with FBCCA and SVMs (more
traditional SSVEP classification methods) in BCIs with small data lengths and
one electrode. This type of BCI can be used to develop small and fast systems.
