  Objective: Target identification in brain-computer interface (BCI) spellers
refers to the electroencephalogram (EEG) classification for predicting the
target character that the subject intends to spell. When the visual stimulus of
each character is tagged with a distinct frequency, the EEG records
steady-state visually evoked potentials (SSVEP) whose spectrum is dominated by
the harmonics of the target frequency. In this setting, we address the target
identification and propose a novel deep neural network (DNN) architecture.
Method: The proposed DNN processes the multi-channel SSVEP with convolutions
across the sub-bands of harmonics, channels, time, and classifies at the fully
connected layer. We test with two publicly available large scale (the benchmark
and BETA) datasets consisting of in total 105 subjects with 40 characters. Our
first stage training learns a global model by exploiting the statistical
commonalities among all subjects, and the second stage fine tunes to each
subject separately by exploiting the individualities. Results: Our DNN achieves
impressive information transfer rates (ITRs) on both datasets, 265.23 bits/min
and 196.59 bits/min, respectively, with only 0.4 seconds of stimulation. The
code is available for reproducibility at
https://github.com/osmanberke/Deep-SSVEP-BCI. Conclusion: The presented DNN
strongly outperforms the state-of-the-art techniques as our accuracy and ITR
rates are the highest ever reported performance results on these datasets.
Significance: Due to its unprecedentedly high speller ITRs and flawless
applicability to general SSVEP systems, our technique has great potential in
various biomedical engineering settings of BCIs such as communication,
rehabilitation and control.
