Toward a reliable PWM‐based light‐emitting diode visual stimulus for improved SSVEP response with minimal visual fatigue

Mouli, Surej; Palaniappan, Ramaswamy

doi:10.1049/joe.2016.0314

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…Each flash frequency for SSVEP that was generated precisely had a duty-cycle of 85% as that duty-cycle gave the highest performance as shown in a previous study [16] . The LED stimulus was driven using high-power MOSFETs (A09T) through a switch-down regulator, MP1584 to provide a constant current source of 3A, to provide optimum brightness throughout the experiment.…”

Section: Hardware Descriptionsupporting

confidence: 56%

DIY hybrid SSVEP-P300 LED stimuli for BCI platform using EMOTIV EEG headset

Mouli

Palaniappan

2020

HardwareX

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Section: Hardware Descriptionsupporting

confidence: 56%

DIY hybrid SSVEP-P300 LED stimuli for BCI platform using EMOTIV EEG headset

Mouli

Palaniappan

2020

HardwareX

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“…This finding is not in line with previous reports showing that a visual stimulus evoking stronger SSVEP responses induced the severer visual fatigue [29–31] when an LCD monitor was used for presenting visual stimuli. However, there are also some evidences showing that the relationship between visual comfort and BCI performance is dependent upon the stimulation rendering device (e.g., light emitting diodes: LEDs) or stimulus types (e.g., SSMVEP) [32, 33]. Our results also suggest that a user's optimal visual stimulus in VR environments might be readily determined by rating the subjective visual comfort of the user even before the main BCI experiment.…”

Section: Discussionmentioning

confidence: 51%

Comparison of Visual Stimuli for Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces in Virtual Reality Environment in terms of Classification Accuracy and Visual Comfort

Choi

Park

2019

Computational Intelligence and Neuroscience

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Recent studies on brain-computer interfaces (BCIs) based on the steady-state visual evoked potential (SSVEP) have demonstrated their use to control objects or generate commands in virtual reality (VR) environments. However, most SSVEP-based BCI studies performed in VR environments have adopted visual stimuli that are typically used in conventional LCD environments without considering the differences in the rendering devices (head-mounted displays (HMDs) used in the VR environments). The proximity between the visual stimuli and the eyes in HMDs can readily cause eyestrain, degrading the overall performance of SSVEP-based BCIs. Therefore, in the present study, we have tested two different types of visual stimuli—pattern-reversal checkerboard stimulus (PRCS) and grow/shrink stimulus (GSS)—on young healthy participants wearing HMDs. Preliminary experiments were conducted to investigate the visual comfort of each participant during the presentation of the visual stimuli. In subsequent online avatar control experiments, we observed considerable differences in the classification accuracy of individual participants based on the type of visual stimuli used to elicit SSVEP. Interestingly, there was a close relationship between the subjective visual comfort score and the online performance of the SSVEP-based BCI: most participants showed better classification accuracy under visual stimulus they were more comfortable with. Our experimental results suggest the importance of an appropriate visual stimulus to enhance the overall performance of the SSVEP-based BCIs in VR environments. In addition, it is expected that the appropriate visual stimulus for a certain user might be readily selected by surveying the user’s visual comfort for different visual stimuli, without the need for the actual BCI experiments.

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“…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%

“…Furthermore, it has also been shown that low-frequency stimuli can cause photosensitivity-based epileptic seizures and high-levels of visual fatigue and discomfort, especially when presented with large number of targets and longer periods [21,43]. Many studies have used high-frequency stimuli and variable duty cycles to reduce visual fatigue and discomfort but at the cost of a decrease in the performance of SSVEP-based BCIs [21,43,[56][57][58]. A few authors have also combined other EEG signals, e.g., P300, with SSVEP to overcome these limitations [22,[59][60][61][62][63].…”

mentioning

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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Toward a reliable PWM‐based light‐emitting diode visual stimulus for improved SSVEP response with minimal visual fatigue

Cited by 10 publications

References 28 publications

DIY hybrid SSVEP-P300 LED stimuli for BCI platform using EMOTIV EEG headset

DIY hybrid SSVEP-P300 LED stimuli for BCI platform using EMOTIV EEG headset

Comparison of Visual Stimuli for Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces in Virtual Reality Environment in terms of Classification Accuracy and Visual Comfort

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

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