A Hybrid Speller Design Using Eye Tracking and SSVEP Brain-Computer Interface
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Author(s)
Mannan, Malik M Naeem
Kamran, M Ahmad
Kang, Shinil
Choi, Hak Soo
Jeong, Myung Yung
Griffith University Author(s)
Year published
2020
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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 ...
View more >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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View more >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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Journal Title
Sensors
Volume
20
Issue
3
Publisher URI
Copyright Statement
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Subject
Human-computer interaction
Software engineering
Analytical chemistry
Ecology
Distributed computing and systems software
Science & Technology
Physical Sciences
Technology
Chemistry, Analytical
Engineering, Electrical & Electronic