Implementing a calibration-free SSVEP-based BCI system with 160 targets

Abstract

Objective. Steady-state visual evoked potential (SSVEP) is an essential paradigm of electroencephalogram based brain–computer interface (BCI). Previous studies in the BCI research field mostly focused on enhancing classification accuracy and reducing stimuli duration. This study, however, concentrated on increasing the number of available targets in the BCI systems without calibration. Approach. Motivated by the idea of multiple frequency sequential coding, we developed a calibration-free SSVEP–BCI system implementing 160 targets by four continuous sinusoidal stimuli that lasted four seconds in total. Taking advantage of the benchmark dataset of SSVEP–BCI, this study optimized an arrangement of stimuli sequences, maximizing the response distance between different stimuli. We proposed an effective classification algorithm based on filter bank canonical correlation analysis. To evaluate the performance of this system, we conducted offline and online experiments using cue-guided selection tasks. Eight subjects participated in the offline experiments, and 12 subjects participated in the online experiments with real-time feedbacks. Main results. Offline experiments indicated the feasibility of the stimulation selection and detection algorithms. Furthermore, the online system achieved an average accuracy of 87.16 ± 11.46% and an information transfer rate of 78.84 ± 15.59 bits min−1. Specifically, seven of 12 subjects accomplished online experiments with accuracy higher than 90%. This study proposed an intact solution of applying numerous targets to SSVEP-based BCIs. Results of experiments confirmed the utility and efficiency of the system. Significance. This study firstly provides a calibration-free SSVEP–BCI speller system that enables more than 100 commands. This system could significantly expand the application scenario of SSVEP-based BCI. Meanwhile, the design criterion can hopefully enhance the overall performance of the BCI system. The demo video can be found in the supplementary material available online at stacks.iop.org/JNE/18/046094/mmedia.

Studies to Overcome Brain–Computer Interface Challenges

“…Over the past decades, there have been lots of BCI studies. They were usually focused on the methods to improve the prediction accuracy [5,8,23,[30][31][32], raise the number of commands [12,33], increase the information transfer rate (ITR) [34][35][36][37][38], or reduce the training efforts [7,30,34,39]. To enhance the prediction accuracy, new classification algorithms [30,40,41] or feature extraction methods have been proposed [31,32,42].…”

“…The BCI study using deep learning shows 99.38% prediction accuracy for motor imagery tasks [30]. Furthermore, various stimulus-presentation methods were suggested to increase the number of commands [12,15,43]. A recent study implemented a speller with 160 characters by combining different frequency signals [12].…”

“…Furthermore, various stimulus-presentation methods were suggested to increase the number of commands [12,15,43]. A recent study implemented a speller with 160 characters by combining different frequency signals [12]. Another critical issue in BCI fields is improving typing speed while maintaining high accuracy.…”

“…They can be used to select one among many options, although a monitor and visual stimuli are needed. SSVEP-based BCI utilizes the fact that the electroencephalography (EEG) intensity increases at the same frequency as the visual stimulus from looking from the user among visual stimuli from blinking at different frequencies [2,12]. A user of the SSVEP-BCI can rapidly select the target among several visual stimuli.…”

Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM

“…At present, the most commonly utilized BCI paradigms based on EEG mainly include steady-state visual evoked potential (SSVEP) [11][12][13], P300 [14,15], motor imagery [16,17], etc. Among them, SSVEP based BCI has the advantages of simple preparation, high classification accuracy, high signal-to-noise ratio, short response time, and fewer training requirements [18]. SSVEP is an oscillating neural response caused by external stimuli 2 of 13 flashing at 5~30 Hz [19].…”