Visual Stimuli for P300-Based Brain-Computer Interfaces: Color, Shape, and Mobility

Grigoryan, Rafael; Krysanova, E. U.; Kirjanov, D A; Kaplan, Arline

doi:10.3103/s0096392518020037

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…This is due to the fact that the person is not aware of the changes occurring in nearby pictures while they are concentrating on the target image. As a second consequence, this leads to a reduction in interference from adjacent stimuli, a decrease in user fatigue, and an increase in user attention [24]. In terms of interference caused by non-targets, the non-face paradigm displays greater levels of non-target interference (dotted line), while both facial paradigms have less interference from non-targets.…”

Section: Discussionmentioning

confidence: 99%

An improved model for the use of facial stimulation in hybrid SSVEP+P300 brain-computer interfaces

Kapgate¹,

K²

2023

World J. Adv. Eng. Technol. Sci.

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Purpose: This research proposes a hybrid BCI that integrates Steady State Visual Evoked Potentials (SSVEP) and Event Related Potentials (P300) simultaneously. We included human facial structure into a visual stimulus to elicit stronger cortical responses in a hybrid SSVEP+P300 BCI. We also discussed the possibilities of triggering one potential with facial stimuli and another with non-facial stimuli. Methods: To elicit SSVEP and P300 responses, non-face and neutral-face stimulus paradigms are presented. We also tested the neutral-face and flicker paradigm, in which non-face stimuli would elicit SSVEP and neutral-face stimuli would elicit P300. Results: The results showed that the latter paradigm evoked more robust cortical potentials, leading to enhanced system accuracy and ITR. The neutral-face and flicker paradigm has an average accuracy of 91.62%, with an average system communication rate of 22.04 bits per second. Conclusions: The author talked about visual stimulus characteristics that might change the way that multiple brain potentials are activated simultaneously and how that affects the individual potentials.

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Section: Discussionmentioning

confidence: 99%

An improved model for the use of facial stimulation in hybrid SSVEP+P300 brain-computer interfaces

Kapgate¹,

K²

2023

World J. Adv. Eng. Technol. Sci.

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“…P300 (Positive wave) 250-350 ms e P300 peak is shorter for color stimuli [77]. It reflects the distribution of attention and decisionmaking [25].…”

Section: P200 (Positive Wave) 150-250 Msmentioning

confidence: 99%

“…Posttraumatic disorders Patients find it difficult to achieve high accuracy because of the need to pay attention continuously [77].…”

Section: Epilepsy and Migrainementioning

confidence: 99%

A Review of EEG and fMRI Measuring Aesthetic Processing in Visual User Experience Research

Rui

2021

Computational Intelligence and Neuroscience

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In human-computer interaction, the visual interaction of user experience (UX) and user interface (UI) plays an important role in enriching the quality of daily life. The purpose of our study analyzes the use of brain-computer interface (BCI), wearable technology, and functional magnetic resonance imaging (fMRI) to explore the aesthetic processing of visual neural response to UI and UX designs. Specifically, this review aims to understand neuroaesthetic processing knowledge, aesthetic appreciation models, and the ways in which visual brain studies can improve the quality of current and future UI and UX designs. Recent research has found that subjective evaluations of aesthetic appreciation produce different results for objective evaluations of brain research analysis. We applied SWOT analysis and examined the advantages and disadvantages of both evaluation methods. Furthermore, we conducted a traditional literature review on topics pertaining to the use of aesthetic processing knowledge in the visual interaction field in terms of art therapy, information visualization, website or mobile applications, and other interactive platforms. Our main research findings from current studies have helped and motivated researchers and designers to use convincing scientific knowledge of brain event-related potential, electroencephalography, and fMRI to understand aesthetic judgment. The key trend finds that many designers, artists, and engineers use artistic BCI technology in the visual interaction experience. Herein, the scientific methods applied in the aesthetic appreciation to human-computer interface are summarized, and the influence of the latest wearable brain technology on visual interaction design is discussed. Furthermore, current possible research entry points for aesthetics, usability, and creativity in UI and UX designs are explicated. The study results have implications for the visual user experience research domain as well as for interaction industries, which produce interactive projects to improve people’s daily lives.

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“…Grigoryan et.al. have recently showed the positive effect of mobile visual stimuli in EEG-based BCIs as well [7].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Natural image reconstruction from brain waves: a novel visual BCI system with native feedback

Rashkov¹,

Bobe²,

Fastovets

et al. 2019

Preprint

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Here we hypothesize that observing the visual stimuli of different categories trigger distinct brain states that can be decoded from noninvasive EEG recordings. We introduce an effective closed-loop BCI system that reconstructs the observed or imagined stimuli images from the co-occurring brain wave parameters. The reconstructed images are presented to the subject as a visual feedback. The developed system is applicable to training BCI-naïve subjects because of the user-friendly and intuitive way the visual patterns are employed to modify the brain states.

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Visual Stimuli for P300-Based Brain-Computer Interfaces: Color, Shape, and Mobility

Cited by 6 publications

References 12 publications

An improved model for the use of facial stimulation in hybrid SSVEP+P300 brain-computer interfaces

An improved model for the use of facial stimulation in hybrid SSVEP+P300 brain-computer interfaces

A Review of EEG and fMRI Measuring Aesthetic Processing in Visual User Experience Research

Natural image reconstruction from brain waves: a novel visual BCI system with native feedback

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