A Deep Neural Network for SSVEP-Based Brain-Computer Interfaces

• Published in: IEEE transactions on biomedical engineering Vol. 69; no. 2; pp. 932 - 944
• Main Authors: Guney, Osman Berke, Oblokulov, Muhtasham, Ozkan, Huseyin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; BCI; Biomedical engineering; Brain; brain-computer interface; Brain-Computer Interfaces; Computer applications; Correlation; Datasets; Deep learning; EEG; Electroencephalography; Evoked Potentials; Evoked Potentials, Visual; Harmonic analysis; Harmonics; Human-computer interface; Humans; Implants; Information transfer; Interfaces; Mathematical models; Neural networks; Neural Networks, Computer; Rehabilitation; Reproducibility of Results; Signal to noise ratio; speller; SSVEP; steady state visually evoked potentials; Steady-state; Target recognition; Training; Visual stimuli
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2021.3110440

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.