Evoked Neural Responses to Events in Video

Rosenthal, Daniel; DeGuzman, Paul; Parra, Lucas C.; Sajda, Paul

doi:10.1109/jstsp.2014.2313022

Cited by 13 publications

(16 citation statements)

References 19 publications

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“…A key feature of the RSVP-BCI paradigm is the rate of presentation; this is particularly relevant as the focus of this paradigm is presenting data to participants at a rapid rate, so that large amounts of data can be analyzed in short time periods. In the literature, the reported duration of stimuli varied from 50 to 500ms [16][32] [33], but the optimal presentation duration/rate is undetermined.…”

Section: Discussionmentioning

confidence: 99%

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

Lees

McCullagh

Payne

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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Rapid serial visual presentation (RSVP) based brain-computer interfaces (BCIs) can detect target images among a continuous stream of rapidly presented images, by classifying a viewer's event related potentials (ERPs) associated with the target and non-targets images. Whilst the majority of RSVP-BCI studies to date have concentrated on the identification of a single type of image, namely pictures, here we study the capability of RSVP-BCI to detect three different target image types: pictures, numbers and words. The impact of presentation duration (speed) i.e., 100-200ms (5-10Hz), 200-300ms (3.3-5Hz) or 300-400ms (2.5-3.3Hz), is also investigated. 2-way repeated measure ANOVA on accuracies of detecting targets from non-target stimuli (ratio 1:9) measured via area under the receiver operator characteristics curve (AUC) for N=15 subjects revealed a significant effect of factor Stimulus-Type (pictures, numbers, words) (F (2,28) = 7.243, p = 0.003) and for Stimulus-Duration (F (2,28) = 5.591, p = 0.011). Furthermore, there is an interaction between stimulus type and duration: F (4,56) = 4.419, p = 0.004). The results indicate that when designing RSVP-BCI paradigms, the content of the images and the rate at which images are presented impact on the accuracy of detection and hence these parameters are key experimental variables in protocol design and applications, which apply RSVP for multimodal image datasets.

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Section: Discussionmentioning

confidence: 99%

“…RSVP-BCIs have been used to detect and recognize target pictures of objects, scenes, people and events in static and motion images [14] [15] [16] [17]. Computers are unable to analyze imagery as efficiently or successfully as people but manual analysis tools are slow [2] [18].…”

Section: Introductionmentioning

confidence: 99%

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

Lees

McCullagh

Payne

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The stream of images presented within an RSVP paradigm comprise frequent non-target images and infrequent target images; different ERP components are associated with target and non-target stimuli (Bigdely-Shamlo et al 2008, Cohen 2014, Sajda et al 2014. BCI signal processing algorithms are used to recognize spatio-temporal electrophysiological responses and link them to target image identification, ideally on a single trial basis (Manor et al 2016).…”

Section: Event Related Potentials and Their Use In Rsvp-based Bcismentioning

confidence: 99%

“…• Participants can be asked to identify a target event; in this case, the target is identified across space and time. The participant is required to integrate features from both motion and form to decide whether a behavior constitutes a target, for example, Rosenthal et al (2014) defined the target as a person leaving a suspicious package in a train station.…”

Section: Movingmentioning

confidence: 99%

A review of rapid serial visual presentation-based brain–computer interfaces

et al. 2018

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Rapid serial visual presentation (RSVP) combined with the detection of event-related brain responses facilitates the selection of relevant information contained in a stream of images presented rapidly to a human. Event related potentials (ERPs) measured non-invasively with electroencephalography (EEG) can be associated with infrequent targets amongst a stream of images. Human-machine symbiosis may be augmented by enabling human interaction with a computer, without overt movement, and/or enable optimization of image/information sorting processes involving humans. Features of the human visual system impact on the success of the RSVP paradigm, but pre-attentive processing supports the identification of target information post presentation of the information by assessing the co-occurrence or time-locked EEG potentials. This paper presents a comprehensive review and evaluation of the limited, but significant, literature on research in RSVP-based brain-computer interfaces (BCIs). Applications that use RSVP-based BCIs are categorized based on display mode and protocol design, whilst a range of factors influencing ERP evocation and detection are analyzed. Guidelines for using the RSVP-based BCI paradigms are recommended, with a view to further standardizing methods and enhancing the inter-relatability of experimental design to support future research and the use of RSVP-based BCIs in practice.

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“…Only a few attempts on decoding ERP or EFRP in videos or virtual reality simulations have been reported, e.g. [15,19]. However, the experimental conditions in those studies did not fully reflect the real-world dynamics.…”

Section: Introductionmentioning

confidence: 99%

Closed-loop EEG study on visual recognition during driving

Aydarkhanov¹,

Uscumlic²,

Chavarriaga³

et al. 2021

J. Neural Eng.

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Objective. In contrast to the classical visual brain–computer interface (BCI) paradigms, which adhere to a rigid trial structure and restricted user behavior, electroencephalogram (EEG)-based visual recognition decoding during our daily activities remains challenging. The objective of this study is to explore the feasibility of decoding the EEG signature of visual recognition in experimental conditions promoting our natural ocular behavior when interacting with our dynamic environment. Approach. In our experiment, subjects visually search for a target object among suddenly appearing objects in the environment while driving a car-simulator. Given that subjects exhibit an unconstrained overt visual behavior, we based our study on eye fixation-related potentials (EFRPs). We report on gaze behavior and single-trial EFRP decoding performance (fixations on visually similar target vs. non-target objects). In addition, we demonstrate the application of our approach in a closed-loop BCI setup. Main results. To identify the target out of four symbol types along a road segment, the BCI system integrated decoding probabilities of multiple EFRP and achieved the average online accuracy of 0.37 ± 0.06 (12 subjects), statistically significantly above the chance level. Using the acquired data, we performed a comparative study of classification algorithms (discriminating target vs. non-target) and feature spaces in a simulated online scenario. The EEG approaches yielded similar moderate performances of at most 0.6 AUC, yet statistically significantly above the chance level. In addition, the gaze duration (dwell time) appears to be an additional informative feature in this context. Significance. These results show that visual recognition of sudden events can be decoded during active driving. Therefore, this study lays a foundation for assistive and recommender systems based on the driver’s brain signals.

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Evoked Neural Responses to Events in Video

Cited by 13 publications

References 19 publications

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

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

A review of rapid serial visual presentation-based brain–computer interfaces

Closed-loop EEG study on visual recognition during driving

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