An SSVEP and Eye Tracking Hybrid BNCI: Potential Beyond Communication and Control

McCullagh, Paul; Brennan, Chris; Lightbody, Gaye; Galway, Leo; Thompson, Eileen; Martin, Suzanne

doi:10.1007/978-3-319-39955-3_7

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…They also showed that an SSVEP-based BCI performed better than an eye tracking-based speller when targets are densely located and small in size. Recently, eye tracking has been combined with EEG to remove ocular artifacts from EEG signals [74,75] and to develop hybrid BCI systems [65,[76][77][78][79][80][81]. All these studies have shown improved performance as compared to EEG only, as well as the feasibility of combining EEG with eye tracking.…”

Section: Introductionmentioning

confidence: 99%

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

Mannan

Kamran

Kang

et al. 2020

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

Mannan

Kamran

Kang

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…McCullagh et al used an EyeTribe tracker (The Eye Tribe ApS, Copenhagen, Denmark) for gaze coordinates and EEG-data recorded with an Emotiv EPOC (Emotiv, San Francisco, CA, USA) for selection [ 24 ]. Their hybrid approach maintained information transfer rate (ITR) while accuracy and efficiency were increased.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Mental Spelling Application Based on Eye Tracking and SSVEP-Based BCI

et al. 2017

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Steady state visual evoked potentials (SSVEPs)-based Brain-Computer interfaces (BCIs), as well as eyetracking devices, provide a pathway for re-establishing communication for people with severe disabilities. We fused these control techniques into a novel eyetracking/SSVEP hybrid system, which utilizes eye tracking for initial rough selection and the SSVEP technology for fine target activation. Based on our previous studies, only four stimuli were used for the SSVEP aspect, granting sufficient control for most BCI users. As Eye tracking data is not used for activation of letters, false positives due to inappropriate dwell times are avoided. This novel approach combines the high speed of eye tracking systems and the high classification accuracies of low target SSVEP-based BCIs, leading to an optimal combination of both methods. We evaluated accuracy and speed of the proposed hybrid system with a 30-target spelling application implementing all three control approaches (pure eye tracking, SSVEP and the hybrid system) with 32 participants. Although the highest information transfer rates (ITRs) were achieved with pure eye tracking, a considerable amount of subjects was not able to gain sufficient control over the stand-alone eye-tracking device or the pure SSVEP system (78.13% and 75% of the participants reached reliable control, respectively). In this respect, the proposed hybrid was most universal (over 90% of users achieved reliable control), and outperformed the pure SSVEP system in terms of speed and user friendliness. The presented hybrid system might offer communication to a wider range of users in comparison to the standard techniques.

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