The Research of Distracting Factors Influence on Quality of Brain-Computer Interface Usage

Cherepanova, Anastasiia; Petrova, Aliona; Voznenko, Timofei I.; Dyumin, Alexander A.; Gridnev, Alexander A.; Chepin, Eugene V.

doi:10.1007/978-3-319-99316-4_6

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…In previous studies where background stimuli were present during the use of BCI, the BCI accuracy was not improved, but in most cases decreased (Lian et al, 2017;Voznenko et al, 2018;Cherepanova et al, 2019;. Especially, the visual BCI performance deteriorated when background stimuli, whether auditory or visual, attracted attention.…”

Section: Discussionmentioning

confidence: 76%

“…Especially, the visual BCI performance deteriorated when background stimuli, whether auditory or visual, attracted attention. Also, the more mental workload was required, the more the accuracy decreased (Cherepanova et al, 2019;. In addition, the presence of background stimuli without any requirement of attention often showed reduced performance in BCI (Lian et al, 2017;Voznenko et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

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Brain-Computer Interfaces for Perception, Learning, and Motor Control

2021

Frontiers Research Topics

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About FrontiersFrontiers is more than just an open-access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers Journal SeriesThe Frontiers Journal Series is a multi-tier and interdisciplinary set of open-access, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers Journal Series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to QualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation.

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Section: Discussionmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Brain-Computer Interfaces for Perception, Learning, and Motor Control

2021

Frontiers Research Topics

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“…In previous studies where background stimuli were present during the use of BCI, the BCI accuracy was not improved, but in most cases decreased (Lian et al, 2017;Voznenko et al, 2018;Cherepanova et al, 2019;Xu et al, 2020). Especially, the visual BCI performance deteriorated when background stimuli, whether auditory or visual, attracted attention.…”

Section: Discussionmentioning

confidence: 77%

“…Especially, the visual BCI performance deteriorated when background stimuli, whether auditory or visual, attracted attention. Also, the more mental workload was required, the more the accuracy decreased ( Cherepanova et al, 2019 ; Xu et al, 2020 ). In addition, the presence of background stimuli without any requirement of attention often showed reduced performance in BCI ( Lian et al, 2017 ; Voznenko et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Effects of Emotional Stimulations on the Online Operation of a P300-Based Brain–Computer Interface

Kim

Heo

et al. 2021

Front. Hum. Neurosci.

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Using P300-based brain–computer interfaces (BCIs) in daily life should take into account the user’s emotional state because various emotional conditions are likely to influence event-related potentials (ERPs) and consequently the performance of P300-based BCIs. This study aimed at investigating whether external emotional stimuli affect the performance of a P300-based BCI, particularly built for controlling home appliances. We presented a set of emotional auditory stimuli to subjects, which had been selected for each subject based on individual valence scores evaluated a priori, while they were controlling an electric light device using a P300-based BCI. There were four conditions regarding the auditory stimuli, including high valence, low valence, noise, and no sound. As a result, subjects controlled the electric light device using the BCI in real time with a mean accuracy of 88.14%. The overall accuracy and P300 features over most EEG channels did not show a significant difference between the four auditory conditions (p > 0.05). When we measured emotional states using frontal alpha asymmetry (FAA) and compared FAA across the auditory conditions, we also found no significant difference (p > 0.05). Our results suggest that there is no clear evidence to support a hypothesis that external emotional stimuli influence the P300-based BCI performance or the P300 features while people are controlling devices using the BCI in real time. This study may provide useful information for those who are concerned with the implementation of a P300-based BCI in practice.

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“…Therefore, it is di cult to document. In addition, although P300 can re ect external visual stimuli within a latency of 300 milliseconds, the latency period of the colour visual interaction is still unclear 22 . Therefore, we set the interaction time windows of each stimulus to 1 second during the experiment.…”

Section: Interactionmentioning

confidence: 99%

Decoding Human Visual Colour EEG Information Using Machine Learning and Visual Evoked Potentials

Zeng

Feng

et al. 2022

Preprint

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With the rapid development of brain-computer interfaces (BCIs), human visual decoding, one of the important research directions of BCIs, has attracted a substantial amount of attention. However, most visual decoding studies have focused on graphic and image decoding. In this paper, we first demonstrate the possibility of building a new kind of task-irrelevant, simple and fast-stimulus BCI-based experimental paradigm that relies on visual evoked potentials (VEPs) during colour observation. Additionally, the features of visual colour information were found through reliable real-time decoding. We selected 9 subjects who did not have colour blindness to participate in our tests. These subjects were asked to observe red, green, and blue screens in turn with an interstimulus interval of 1 second. The machine learning results showed that the visual colour classification accuracy had a maximum of 93.73%. The latency evoked by visual colour stimuli was within the P300 range, i.e., 176.8 milliseconds for the red screen, 206.5 milliseconds for the green screen, and 225.3 milliseconds for the blue screen. The experimental results hereby show that the VEPs can be used for reliable colour real-time decoding.

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The Research of Distracting Factors Influence on Quality of Brain-Computer Interface Usage

Cited by 7 publications

References 9 publications

Brain-Computer Interfaces for Perception, Learning, and Motor Control

Brain-Computer Interfaces for Perception, Learning, and Motor Control

Effects of Emotional Stimulations on the Online Operation of a P300-Based Brain–Computer Interface

Decoding Human Visual Colour EEG Information Using Machine Learning and Visual Evoked Potentials

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