  Objective: To propose novel SSVEP classification methodologies using deep
neural networks (DNNs) and improve performances in single-channel and
user-independent brain-computer interfaces (BCIs) with small data lengths.
Approach: We propose the utilization of filter banks (creating sub-band
components of the EEG signal) in conjunction with DNNs. In this context, we
created three different models: a recurrent neural network (FBRNN) analyzing
the time domain, a 2D convolutional neural network (FBCNN-2D) processing
complex spectrum features and a 3D convolutional neural network (FBCNN-3D)
analyzing complex spectrograms, which we introduce in this study as possible
input for SSVEP classification. We tested our neural networks on three open
datasets and conceived them so as not to require calibration from the final
user, simulating a user-independent BCI. Results: The DNNs with the filter
banks surpassed the accuracy of similar networks without this preprocessing
step by considerable margins, and they outperformed common SSVEP classification
methods (SVM and FBCCA) by even higher margins. Conclusion and significance:
Filter banks allow different types of deep neural networks to more efficiently
analyze the harmonic components of SSVEP. Complex spectrograms carry more
information than complex spectrum features and the magnitude spectrum, allowing
the FBCNN-3D to surpass the other CNNs. The performances obtained in the
challenging classification problems indicates a strong potential for the
construction of portable, economical, fast and low-latency BCIs.
