A Hybrid Asynchronous Brain-Computer Interface Combining SSVEP and EOG Signals

• Published in: IEEE transactions on biomedical engineering Vol. 67; no. 10; pp. 2881 - 2892
• Main Authors: Zhou, Yajun, He, Shenghong, Huang, Qiyun, Li, Yuanqing
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Asynchronous brain-computer interface (BCI); Blinking; Brain; Brain-computer interfaces; Computer applications; Correlation analysis; EEG; Electroencephalography; electroencephalography (EEG); Electrooculography; electrooculography (EOG); Eye (anatomy); eye blink; Flicker; Frequency modulation; Graphical user interface; Graphical user interfaces; Human-computer interface; Hybrid systems; Implants; Information transfer; Response time; steady-state visual evoked potential (SSVEP); Target recognition; Time factors; Visual evoked potentials; Visual signals; Visualization
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2020.2972747

Objective: A challenging task for an electroencephalography (EEG)-based asynchronous brain-computer interface (BCI) is to effectively distinguish between the idle state and the control state while maintaining a short response time and a high accuracy when commands are issued in the control state. This study proposes a novel hybrid asynchronous BCI system based on a combination of steady-state visual evoked potentials (SSVEPs) in the EEG signal and blink-related electrooculography (EOG) signals. Methods: Twelve buttons corresponding to 12 characters are included in the graphical user interface (GUI). These buttons flicker at different fixed frequencies and phases to evoke SSVEPs and are simultaneously highlighted by changing their sizes. The user can select a character by focusing on its frequency-phase stimulus and simultaneously blinking his/her eyes in accordance with its highlighting as his/her EEG and EOG signals are recorded. A multifrequency band-based canonical correlation analysis (CCA) method is applied to the EEG data to detect the evoked SSVEPs, whereas the EOG data are analyzed to identify the user's blinks. Finally, the target character is identified based on the SSVEP and blink detection results. Results: Ten healthy subjects participated in our experiments and achieved an average information transfer rate (ITR) of 105.52 bits/min, an average accuracy of 95.42%, an average response time of 1.34 s and an average false-positive rate (FPR) of 0.8%. Conclusion: The proposed BCI generates multiple commands with a high ITR and low FPR. Significance: The hybrid asynchronous BCI has great potential for practical applications in communication and control.