A Novel Auditory-tactile P300-based BCI Paradigm

Jiang, Jing; Zhang, Boyang; Yin, Erwei; Wang, Chunhui; Deng, Baosong

doi:10.1109/civemsa45640.2019.9071600

Cited by 4 publications

(4 citation statements)

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“…According to the 2 -values and ERP waveforms observed in the current study, The ERP component that appears at approximately 500 ms in Pz, or the late positive potential (LPP), is important for classification during the AV and V conditions. However, P300 enhancement was not observed, in contrast to the findings of some previous multimodal P300based BCI studies [24,23,8]. Nonetheless, other researchers have reported decreases in P300 [23].…”

Section: Discussioncontrasting

confidence: 92%

“…However, in these studies, BCIs used visual stimuli that appeared at different locations on the monitor (i.e., non-RSMP), while visual stimuli were presented only at the cen- ter of the monitor in the current study. This difference in presentation may cause target and nontarget ERP waveforms and enhancement of the P300 at Fz when using auditorytactile stimuli [8]. In our study, P300 amplitude was not enhanced by audiovisual stimuli.…”

Section: Discussioncontrasting

confidence: 48%

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Brain-computer interface with rapid serial multimodal presentation using artificial facial images and voice

Onishi

2021

Preprint

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Section: Discussioncontrasting

confidence: 92%

Section: Discussioncontrasting

confidence: 48%

Brain-computer interface with rapid serial multimodal presentation using artificial facial images and voice

Onishi

2021

Preprint

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“…Despite its simplicity, LDA often brought out robust and acceptable classification results for previous EVE-BCIs. Depending on various parameter settings, other deformed DAs such as Bayesian Linear Discriminant Analysis (BLDA) [ 1 , 4 , 36 , 75 ], Shrinkage LDA [ 30 , 31 , 38 , 55 , 86 ], Regularized Discriminant Analysis (RDA)) [ 24 , 33 , 42 , 43 , 51 , 62 , 99 ], and step-wise Linear Discriminant Analysis (SWLDA) [ 74 , 88 , 91 , 92 , 98 ] were also used. In terms of popularity, followed by the DA, another robust supervised learning model, linear Support Vector Machine (linear SVM), was also implemented in [ 32 , 33 , 73 , 82 , 84 , 89 , 95 ].…”

Section: Resultsmentioning

confidence: 99%

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review

Huang

Liang

et al. 2022

PLoS ONE

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Recently, a novel electroencephalogram-based brain-computer interface (EVE-BCI) using the vibrotactile stimulus shows great potential for an alternative to other typical motor imagery and visual-based ones. (i) Objective: in this review, crucial aspects of EVE-BCI are extracted from the literature to summarize its key factors, investigate the synthetic evidence of feasibility, and generate recommendations for further studies. (ii) Method: five major databases were searched for relevant publications. Multiple key concepts of EVE-BCI, including data collection, stimulation paradigm, vibrotactile control, EEG signal processing, and reported performance, were derived from each eligible article. We then analyzed these concepts to reach our objective. (iii) Results: (a) seventy-nine studies are eligible for inclusion; (b) EEG data are mostly collected among healthy people with an embodiment of EEG cap in EVE-BCI development; (c) P300 and Steady-State Somatosensory Evoked Potential are the two most popular paradigms; (d) only locations of vibration are heavily explored by previous researchers, while other vibrating factors draw little interest. (e) temporal features of EEG signal are usually extracted and used as the input to linear predictive models for EVE-BCI setup; (f) subject-dependent and offline evaluations remain popular assessments of EVE-BCI performance; (g) accuracies of EVE-BCI are significantly higher than chance levels among different populations. (iv) Significance: we summarize trends and gaps in the current EVE-BCI by identifying influential factors. A comprehensive overview of EVE-BCI can be quickly gained by reading this review. We also provide recommendations for the EVE-BCI design and formulate a checklist for a clear presentation of the research work. They are useful references for researchers to develop a more sophisticated and practical EVE-BCI in future studies.

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“…This cross-modal system proved that the audio stimuli could compensate for the visual stimuli when users got tired of staring at the screen for long periods of time. In Jiang et al ( 2019 ), Jiang et al proposed an auditory-tactile P300 speller. Although the performance of the visual independent BCI system was worse than those depending on visualization, it was significant for patients with severe visual dysfunction.…”

Section: Methodsmentioning

confidence: 99%

Advances in P300 brain–computer interface spellers: toward paradigm design and performance evaluation

Pan

Chen

Ban

et al. 2022

Front. Hum. Neurosci.

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A brain-computer interface (BCI) is a non-muscular communication technology that provides an information exchange channel for our brains and external devices. During the decades, BCI has made noticeable progress and has been applied in many fields. One of the most traditional BCI applications is the BCI speller. This article primarily discusses the progress of research into P300 BCI spellers and reviews four types of P300 spellers: single-modal P300 spellers, P300 spellers based on multiple brain patterns, P300 spellers with multisensory stimuli, and P300 spellers with multiple intelligent techniques. For each type of P300 speller, we further review several representative P300 spellers, including their design principles, paradigms, algorithms, experimental performance, and corresponding advantages. We particularly emphasized the paradigm design ideas, including the overall layout, individual symbol shapes and stimulus forms. Furthermore, several important issues and research guidance for the P300 speller were identified. We hope that this review can assist researchers in learning the new ideas of these novel P300 spellers and enhance their practical application capability.

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A Novel Auditory-tactile P300-based BCI Paradigm

Cited by 4 publications

References 11 publications

Brain-computer interface with rapid serial multimodal presentation using artificial facial images and voice

Brain-computer interface with rapid serial multimodal presentation using artificial facial images and voice

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review

Advances in P300 brain–computer interface spellers: toward paradigm design and performance evaluation

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