Speed of Rapid Serial Visual Presentation of Pictures, Numbers and Words Affects Event-Related Potential-Based Detection Accuracy

Lees, Stephanie; McCullagh, Paul; Payne, Phillippa; Maguire, Liam; Lotte, Fabien; Coyle, Damien

doi:10.1109/tnsre.2019.2953975

Cited by 22 publications

(19 citation statements)

References 52 publications

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“…The exogenous ERP component will look small if the SOA is short because the overlap of each stimulus is large. Researchers have also examined the influence of speed on RSVP, reporting that the area under the curve decreases as SOA increases [11]. However, no studies have investigated SOAs longer than 500 ms.…”

Section: Discussionmentioning

confidence: 99%

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

Onishi

2021

Preprint

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Section: Discussionmentioning

confidence: 99%

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

Onishi

2021

Preprint

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“…On one hand, the dataset can be used to design system diagrams toward different applications. The optimization of parameters is very important for the design and implementation of a practical BCI system ( Zhang and Gao, 2019 ; Lees et al, 2020 ). For example, the effect of time interval between target images on EEG characteristics can not only inspire the design of optimal RSVP stimulation paradigm but also deepen the understanding of attentional blink.…”

Section: Discussionmentioning

confidence: 99%

A Benchmark Dataset for RSVP-Based Brain–Computer Interfaces

Zhang

Wang

Zhang³

et al. 2020

Front. Neurosci.

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This paper reports on a benchmark dataset acquired with a brain–computer interface (BCI) system based on the rapid serial visual presentation (RSVP) paradigm. The dataset consists of 64-channel electroencephalogram (EEG) data from 64 healthy subjects (sub1,…, sub64) while they performed a target image detection task. For each subject, the data contained two groups (“A” and “B”). Each group contained two blocks, and each block included 40 trials that corresponded to 40 stimulus sequences. Each sequence contained 100 images presented at 10 Hz (10 images per second). The stimulus images were street-view images of two categories: target images with human and non-target images without human. Target images were presented randomly in the stimulus sequence with a probability of 1∼4%. During the stimulus presentation, subjects were asked to search for the target images and ignore the non-target images in a subjective manner. To keep all original information, the dataset was the raw continuous data without any processing. On one hand, the dataset can be used as a benchmark dataset to compare the algorithms for target identification in RSVP-based BCIs. On the other hand, the dataset can be used to design new system diagrams and evaluate their BCI performance without collecting any new data through offline simulation. Furthermore, the dataset also provides high-quality data for characterizing and modeling event-related potentials (ERPs) and steady-state visual evoked potentials (SSVEPs) in RSVP-based BCIs. The dataset is freely available from http://bci.med.tsinghua.edu.cn/download.html .

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“…Moreover, by studying the history of the spellers, we found that most work done in this field was presented in a matrix structure ( Lees et al, 2018 , 2020 ; Mijani et al, 2019 ). In these protocols, the subject should look at different points on the screen during the experiment.…”

Section: Discussionmentioning

confidence: 99%

Improving the Cross-Subject Performance of the ERP-Based Brain–Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank

Liu

Wang

Sheng

et al. 2020

Front. Hum. Neurosci.

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The brain-computer interface (BCI) is a system that is designed to provide communication channels to anyone through a computer. Initially, it was suggested to help the disabled, but actually had been proposed a wider range of applications. However, the cross-subject recognition in BCI systems is difficult to break apart from the individual specific characteristics, unsteady characteristics, and environmental specific characteristics, which also makes it difficult to develop highly reliable and highly stable BCI systems. Rapid serial visual presentation (RSVP) is one of the most recent spellers with a clean, unified background and a single stimulus, which may evoke event-related potential (ERP) patterns with less individual difference. In order to build a BCI system that allows new users to use it directly without calibration or with less calibration time, RSVP was employed as evoked paradigm, then correlation analysis rank (CAR) algorithm was proposed to improve the cross-individual classification and simultaneously use as less training data as possible. Fifty-eight subjects took part in the experiments. The flash stimulation time is 200 ms, and the off time is 100 ms. The P300 component was locked to the target representation by time. The results showed that RSVP could evoke more similar ERP patterns among subjects compared with matrix paradigm. Then, the included angle cosine was calculated and counted for averaged ERP waveform between each two subjects. The average matching number of all subjects was 6 for the matrix paradigm, while for the RSVP paradigm, the average matching number range was 20 when the threshold value was set to 0.5, more than three times as much larger, quantificationally indicating that ERP waveforms evoked by the RSVP paradigm produced smaller individual differences, and it is more favorable for crosssubject classification. Information transfer rates (ITR) were also calculated for RSVP and matrix paradigms, and the RSVP paradigm got the average ITR of 43.18 bits/min, which was 13% higher than the matrix paradigm. Then, the receiver operating characteristic (ROC) curve value was computed and compared using the proposed CAR algorithm and traditional random selection. The results showed that the proposed CAR got significantly better performance than the traditional random selection and got the best AUC value

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Speed of Rapid Serial Visual Presentation of Pictures, Numbers and Words Affects Event-Related Potential-Based Detection Accuracy

Cited by 22 publications

References 52 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

A Benchmark Dataset for RSVP-Based Brain–Computer Interfaces

Improving the Cross-Subject Performance of the ERP-Based Brain–Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank

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