Asynchronous Control of ERP-Based BCI Spellers Using Steady-State Visual Evoked Potentials Elicited by Peripheral Stimuli

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 27; no. 9; pp. 1883 - 1892
• Main Authors: Santamaria-Vazquez, Eduardo, Martinez-Cagigal, Victor, Gomez-Pilar, Javier, Hornero, Roberto
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; asynchrony; Attention; Brain; Brain-Computer Interfaces; Calibration; Communication Aids for Disabled; Computer applications; control-state detection; Electrodes; Electroencephalography; Europe; Event-related potentials; Event-Related Potentials, P300 - physiology; Evoked potentials; Evoked Potentials - physiology; Evoked Potentials, Visual - physiology; Female; Human-computer interface; Humans; Implants; Internet; Male; P300; Signal-To-Noise Ratio; Steady state; steady-state visual evoked potentials; Test procedures; Transient analysis; Visual evoked potentials; Visual stimuli; Visualization
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2019.2934645

Brain-computer interface (BCI) spellers based on event related potentials (ERPs) are intrinsically synchronous systems. Therefore, selections are constantly made, even when users are not paying attention to the stimuli. This poses a major limitation in real-life applications, in which an asynchronous control is required. The aim of this study is to design, develop and test a novel method to discriminate whether the user is controlling the system (i.e., control state) or is engaged in other task (i.e., non-control state). To achieve such an asynchronous control, our method detects the steady-state visual evoked potentials (SSVEPs) elicited by peripheral stimuli of ERP-based spellers. A characterization experiment was conducted with 5 subjects to investigate general aspects of this phenomenon. Then, the proposed method was validated with 15 subjects in offline and online sessions. Results show that the proposed method provides a reliable asynchronous control, achieving an average accuracy of 95.5% for control state detection during the online sessions. Furthermore, our approach is independent of the ERP classification stage, and to the best of our knowledge, is the first procedure that does not need to extend the duration of the calibration sessions to acquire non-control observations.