Binocular Phase-Coded Visual Stimuli for SSVEP-Based BCI

Kramberger, Iztok; Kačič, Zdravko; Donaj, Gregor

doi:10.1109/access.2019.2910737

Cited by 5 publications

(5 citation statements)

References 35 publications

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“…Previous studies of EEG signals described that decreased beta (13-30 Hz) band power has been linked to visual attention (mental focus state) [24][25][26]. The SSVEP-BCI study investigated that the occipital lobe of the brain shows a significant role in SSVEP-BCI technologies [27]. Early studies of the working memory task found that the EEG power of the beta (13-30 Hz) band increased in the parietal and occipital lobes [23,28].…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we used high temporal resolution electroencephalography (EEG) method to investigate the mental focus and lost-in-thought states of the BCI users. The first time our study explored the EEG neural marker including delta (1-4 Hz), theta (4-7 Hz), alpha (8)(9)(10)(11)(12), and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) bands in mental focus and lost-in-thought states at frontal and occipital lobes of the brain than in previous studies of SSVEP and EEG [6,[37][38][39]. According to our hypothesis, the power spectrum of EEG signals in mental focus state will increase more than in lost-in-thought state at the frontal lobe, because the frontal lobe is related to attention and working memory.…”

Section: Introductionmentioning

confidence: 99%

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Exploration of User’s Mental State Changes during Performing Brain–Computer Interface

Chikara

Lee

et al. 2020

Sensors

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Substantial developments have been established in the past few years for enhancing the performance of brain–computer interface (BCI) based on steady-state visual evoked potential (SSVEP). The past SSVEP-BCI studies utilized different target frequencies with flashing stimuli in many different applications. However, it is not easy to recognize user’s mental state changes when performing the SSVEP-BCI task. What we could observe was the increasing EEG power of the target frequency from the user’s visual area. BCI user’s cognitive state changes, especially in mental focus state or lost-in-thought state, will affect the BCI performance in sustained usage of SSVEP. Therefore, how to differentiate BCI users’ physiological state through exploring their neural activities changes while performing SSVEP is a key technology for enhancing the BCI performance. In this study, we designed a new BCI experiment which combined working memory task into the flashing targets of SSVEP task using 12 Hz or 30 Hz frequencies. Through exploring the EEG activity changes corresponding to the working memory and SSVEP task performance, we can recognize if the user’s cognitive state is in mental focus or lost-in-thought. Experiment results show that the delta (1–4 Hz), theta (4–7 Hz), and beta (13–30 Hz) EEG activities increased more in mental focus than in lost-in-thought state at the frontal lobe. In addition, the powers of the delta (1–4 Hz), alpha (8–12 Hz), and beta (13–30 Hz) bands increased more in mental focus in comparison with the lost-in-thought state at the occipital lobe. In addition, the average classification performance across subjects for the KNN and the Bayesian network classifiers were observed as 77% to 80%. These results show how mental state changes affect the performance of BCI users. In this work, we developed a new scenario to recognize the user’s cognitive state during performing BCI tasks. These findings can be used as the novel neural markers in future BCI developments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploration of User’s Mental State Changes during Performing Brain–Computer Interface

Chikara

Lee

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…However, recent advances in SSVEP-based BCIs have developed techniques and algorithms to overcome these obstacles. Several studies have proposed different stimulus design techniques to realize large number of targets with only few frequencies, but they have also suffered limitations [21,38,43,50,57,58,90]. Furthermore, a few researchers combined SSVEPs with P300 to generate more targets with less frequencies, but this was at the cost of task complexity, which eventually affected the performance of the system.…”

Section: Discussionmentioning

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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“…The phase defines the point of one cycle at which a wave is or starts. In SSVEP BCI, a phase shift can be used as a distinction between targets, i.e., as a coding method [46,70].…”

Section: Wave Parametersmentioning

confidence: 99%

Optimal Stimulus Properties for Steady-State Visually Evoked Potential Brain–Computer Interfaces: A Scoping Review

Reitelbach,

Oyibo

2024

MTI

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Brain–computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) have been well researched due to their easy system configuration, little or no user training and high information transfer rates. To elicit an SSVEP, a repetitive visual stimulus (RVS) is presented to the user. The properties of this RVS (e.g., frequency, luminance) have a significant influence on the BCI performance and user comfort. Several studies in this area in the last one-and-half decades have focused on evaluating different stimulus parameters (i.e., properties). However, there is little research on the synthesis of the existing studies, as the last review on the subject was published in 2010. Consequently, we conducted a scoping review of related studies on the influence of stimulus parameters on SSVEP response and user comfort, analyzed them and summarized the findings considering the physiological and neurological processes associated with BCI performance. In the review, we found that stimulus type, frequency, color contrast, luminance contrast and size/shape of the retinal image are the most important stimulus properties that influence SSVEP response. Regarding stimulus type, frequency and luminance, there is a trade-off between the best SSVEP response quality and visual comfort. Finally, since there is no unified measuring method for visual comfort and a lack of differentiation in the high-frequency band, we proposed a measuring method and a division of the band. In summary, the review highlights which stimulus properties are important to consider when designing SSVEP BCIs. It can be used as a reference point for future research in BCI, as it will help researchers to optimize the design of their SSVEP stimuli.

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Binocular Phase-Coded Visual Stimuli for SSVEP-Based BCI

Cited by 5 publications

References 35 publications

Exploration of User’s Mental State Changes during Performing Brain–Computer Interface

Exploration of User’s Mental State Changes during Performing Brain–Computer Interface

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

Optimal Stimulus Properties for Steady-State Visually Evoked Potential Brain–Computer Interfaces: A Scoping Review

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