Multiclass Steady-State Visual Evoked Potential Frequency Evaluation Using Chirp-Modulated Stimuli

• Published in: IEEE transactions on human-machine systems Vol. 46; no. 4; pp. 593 - 600
• Main Authors: Waytowich, Nicholas R., Krusienski, Dean J.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Arrays; Brain–computer interfaces (BCIs); canonical correlation analysis (CCA); Chirp; Correlation; Electroencephalography; Evoked potentials; feature selection; genetic algorithms (GAs); Human-computer interface; Light emitting diodes; Optimization; Oscillations; steady-state visual evoked potentials (SSVEPs); Stimulation; Time-frequency analysis; Visual; Visualization
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2015.2513014

Steady-state visual evoked potentials (SSVEPs) are oscillations of the electroencephalogram (EEG) which are mainly observed over the occipital area that exhibits a frequency corresponding to a repetitively flashing visual stimulus. SSVEPs have proven to be very consistent and reliable signals for rapid EEG-based brain-computer interface (BCI) control. While a subject-specific SSVEP stimulus frequency optimization is ideal, this can be a tedious and time-consuming process. Thus, many studies select SSVEP stimulation frequencies somewhat arbitrarily. There is no standardized set of SSVEP stimulus frequencies or frequency selection method, and some studies even claim conflicting frequency ranges for optimal performance. In this work, 17 subjects were stimulated with an LED array that flashed according to a chirp-modulated signal having a frequency that varied linearly over the typical functional range of SSVEP. The resulting EEG was analyzed using canonical correlation analysis and a genetic algorithm was implemented to determine generalized stimulation frequency sets over a continuum of simulated multiclass BCI classification scenarios. The results show that distinct frequency feature groupings exist over the different multiclass scenarios, and that these groupings result in different information transfer rates. These offline results can provide a guide for generalized stimulus frequency selection for SSVEP-based BCIs with an arbitrary number of targets.