A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface

Abstract

Steady-state visual evoked potentials (SSVEPs) have been extensively utilized to develop brain–computer interfaces (BCIs) due to the advantages of robustness, large number of commands, high classification accuracies, and information transfer rates (ITRs). However, the use of several simultaneous flickering stimuli often causes high levels of user discomfort, tiredness, annoyingness, and fatigue. Here we propose to design a stimuli-responsive hybrid speller by using electroencephalography (EEG) and video-based eye-tracking to increase user comfortability levels when presented with large numbers of simultaneously flickering stimuli. Interestingly, a canonical correlation analysis (CCA)-based framework was useful to identify target frequency with a 1 s duration of flickering signal. Our proposed BCI-speller uses only six frequencies to classify forty-eight targets, thus achieve greatly increased ITR, whereas basic SSVEP BCI-spellers use an equal number of frequencies to the number of targets. Using this speller, we obtained an average classification accuracy of 90.35 ± 3.597% with an average ITR of 184.06 ± 12.761 bits per minute in a cued-spelling task and an ITR of 190.73 ± 17.849 bits per minute in a free-spelling task. Consequently, our proposed speller is superior to the other spellers in terms of targets classified, classification accuracy, and ITR, while producing less fatigue, annoyingness, tiredness and discomfort. Together, our proposed hybrid eye tracking and SSVEP BCI-based system will ultimately enable a truly high-speed communication channel.

Spintronic Sensors Based on Magnetic Tunnel Junctions for Wireless Eye Movement Gesture Control

“…To account for the standard deviation of 17, the ITR number is shown to be affected by the nature of the Tetris game interface, where each trial was a self-paced open task and hence the duration of each trial and number of total commands varied between each player. To compare with other modalities, such as brain computer interfacing with electroencephalography, bit rate of 190 bits/min was recently achieved [66]. The bitrate is relatively higher and maybe attributed to the high number of target commands (48) whilst maintaining a similar accuracy of 90.3%, compared to our system accuracy at 90.8%.…”

A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

“…In comparison with the previous relevant research [ 29 , 30 , 44 , 45 , 46 , 47 ] (listed in Table 3 ), instead of using the eye-tracking to detect the eye movement, this study applied the eye blinking to be a trigger in this control system. In the eye-tracking system, the extra device is required to monitor the eye movement.…”

“…In the eye-tracking system, the extra device is required to monitor the eye movement. As demonstrated in the hybrid eye-tracking and SSVEP system [ 30 ], the participants need to wear the extra video eye-tracking system, for which the threshold of velocity, acceleration, and minimum deflection was 30°/s, 8000°/s 2 , and 0.1°, respectively. Since the eyeblink and the SSVEP signal can be acquired from the EEG device with the same channel, the unfriendly hardware burden and extra cost of the hybrid device (e.g., EEG and eye-tracking device) will be reduced.…”

“…A review article [ 28 ] proved that hybrid BCI systems could provide more commands and provide the potential to increases the classification accuracy and information transfer rates. It was also determined by recent literature [ 29 , 30 ] on the hybrid BCI system, which used EEG (i.e., SSVEP and MI) and eye blink/movement signals as the input for a speller.…”

Evaluating the Effect of Stimuli Color and Frequency on SSVEP

“…On the other hand, in [21] researchers tested 10 colors by means of spectral analysis for amplitudes, concluding that colors with large wavelengths, such as red and orange, would capture more attention and generate SSVEP of a higher amplitude, than shorter wavelength colors such as blue and purple. In a review of 57 articles on visual stimulation in BCI systems with the SSVEP paradigm, in [19] researchers classified stimulation frequencies into three bands: low (1-12 Hz), medium (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and high . The low and medium frequencies have, according to the authors, the disadvantage of causing more visual fatigue, and of interfering with the spontaneous activity of the brain, while the high ones are better in these aspects, although they generate SSVEP of a lesser amplitude.…”