Planning to revisit: neural activity in refixation precursors

Nikolaev, Andrey R.; Giannini, Marcello; Rn, Meghanathan; Leeuwen, Cees van

doi:10.1101/660308

Cited by 4 publications

(6 citation statements)

References 136 publications

(383 reference statements)

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“…It is well-developed after saccades in free-viewing conditions (Kamienkowski et al 2012, Nikolaev et al 2016, Ries et al 2018 and even after microsaccades (Dimigen et al 2009). This component is highly sensitive to factors irrelevant to intention (Nikolaev et al 2016). Inspection of the triggeringrelated ERP (figure 3(b)) showed that in our data the lambda-wave latency was about −500 ms. Due to its jitter relative to the triggering time, it could likely affect intervals closer to the triggering.…”

Section: Feature Extractionmentioning

confidence: 46%

“…One of them should be the lambda wave, a postsaccade positive peak (Thickbroom et al 1991, Kazai andYagi 2003). It is well-developed after saccades in free-viewing conditions (Kamienkowski et al 2012, Nikolaev et al 2016, Ries et al 2018 and even after microsaccades (Dimigen et al 2009). This component is highly sensitive to factors irrelevant to intention (Nikolaev et al 2016).…”

Section: Feature Extractionmentioning

confidence: 99%

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A passive BCI for monitoring the intentionality of the gaze-based moving object selection

et al. 2021

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Objective. The use of an electroencephalogram (EEG) anticipation-related component, the expectancy wave (E-wave), in brain–machine interaction was proposed more than 50 years ago. This possibility was not explored for decades, but recently it was shown that voluntary attempts to select items using eye fixations, but not spontaneous eye fixations, are accompanied by the E-wave. Thus, the use of the E-wave detection was proposed for the enhancement of gaze interaction technology, which has a strong need for a mean to decide if a gaze behavior is voluntary or not. Here, we attempted at estimating whether this approach can be used in the context of moving object selection through smooth pursuit eye movements. Approach. Eighteen participants selected, one by one, items which moved on a computer screen, by gazing at them. In separate runs, the participants performed tasks not related to voluntary selection but also provoking smooth pursuit. A low-cost consumer-grade eye tracker was used for item selection. Main results. A component resembling the E-wave was found in the averaged EEG segments time-locked to voluntary selection events of every participant. Linear discriminant analysis with shrinkage regularization classified the intentional and spontaneous smooth pursuit eye movements, using single-trial 300 ms long EEG segments, significantly above chance in eight participants. When the classifier output was averaged over ten subsequent data segments, median group ROC AUC of 0.75 was achieved. Significance. The results suggest the possible usefulness of the E-wave detection in the gaze-based selection of moving items, e.g. in video games. This technique might be more effective when trial data can be averaged, thus it could be considered for use in passive interfaces, for example, in estimating the degree of the user’s involvement during gaze-based interaction.

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Section: Feature Extractionmentioning

confidence: 46%

Section: Feature Extractionmentioning

confidence: 99%

A passive BCI for monitoring the intentionality of the gaze-based moving object selection

et al. 2021

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“…In this study, we model both temporal overlap of neural processes in time and non-linear influences of eye movement parameters (e.g., saccade amplitude or saccade position), which can lead to systematic differences between conditions (Dimigen et al, 2011;Dimigen & Ehinger, 2021;Nikolaev et al, 2016). Such regressionbased deconvolution models are increasingly becoming popular (e.g., Cornelissen et al, 2019;Dandekar et al, 2012;Dimigen & Ehinger, 2021;Kristensen et al, 2017;Smith & Kutas, 2015).…”

Section: A New Methodology That Allows For This Type Of Analysismentioning

confidence: 99%

WildLab: A naturalistic free viewing experiment reveals previously unknown electroencephalography signatures of face processing

Gert

Ehinger

Timm

et al. 2022

Eur J of Neuroscience

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Neural mechanisms of face perception are predominantly studied in wellcontrolled experimental settings that involve random stimulus sequences and fixed eye positions. Although powerful, the employed paradigms are far from what constitutes natural vision. Here, we demonstrate the feasibility of ecologically more valid experimental paradigms using natural viewing behaviour, by combining a free viewing paradigm on natural scenes, free of photographer bias, with advanced data processing techniques that correct for overlap effects and co-varying non-linear dependencies of multiple eye movement parameters. We validate this approach by replicating classic N170 effects in neural responses, triggered by fixation onsets (fixation event-related potentials [fERPs]). Importantly, besides finding a strong correlation between both experiments, our more natural stimulus paradigm yielded smaller variability between subjects than the classic setup. Moving beyond classic temporal and spatial effect locations, our experiment furthermore revealed previously unknown signatures of face processing: This includes category-specific modulation of the event-related potential (ERP)'s amplitude even before fixation onset, as well as adaptation effects across subsequent fixations depending on their history.K E Y W O R D S event-related potentials (ERPs), eye movements, face processing, fixation-related potentials, natural images | INTRODUCTIONThe electroencephalography (EEG) correlates of face processing have been studied widely over the last decades, as faces represent an important stimulus category in our everyday life. Using well-controlled

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“…The main use of BCI systems in Medicine is in therapy (Biasiucci et al, 2018;Bundy et al, 2017;Kevric & Subasi, 2017;Keynan et al, 2016;Kilicarslan et al, 2016;Kraus, Naros, Bauer, Khademi, et al, 2016;Mrachacz-Kersting et al, 2016;Naros et al, 2016), rehabilitation (Ang & Guan, 2017;Frolov et al, 2017;Kevric & Subasi, 2017;Luu et al, 2016;Schirrmeister et al, 2017), improving treadmill walking (Bradford et al, 2016;Luu et al, 2016;Mrachacz-Kersting et al, 2016;Nathan & Contreras-Vidal, 2016), assistive devices (Buccino et al, 2016;Mirkovic et al, 2016;Pandarinath et al, 2017;Riener, 2016), neurofeedback (Keynan et al, 2016;Paret et al, 2016), diagnosis (Keynan et al, 2016;Kilicarslan et al, 2016), physiotherapy , emotion regulation (Keynan et al, 2016;Paret et al, 2016), self-regulation (Naros et al, 2016), neurorehabilitation (Kraus, Naros, Bauer, Khademi, et al, 2016), hearing (Bleichner & Debener, 2017;van Eyndhoven et al, 2017;Yin et al, 2016), and vision (Nikolaev et al, 2016).…”

Section: Medicine and Health Carementioning

confidence: 99%

“…Other researchers focused on improving decoding (Bleichner et al, 2016;Branco et al, 2017;Brandman et al, 2017;Bundy et al, 2016;Edelman et al, 2016;Fuglsang et al, 2017;Grootswagers et al, 2017;Hong & Santosa, 2016;Kilicarslan et al, 2016;Luu et al, 2016;Schirrmeister et al, 2017), artifact removals (Arico et al, 2018;Gaur et al, 2018;Hsu et al, 2016;Kilicarslan et al, 2016;Maddirala & Shaik, 2016;Minguillon et al, 2017;Nakanishi et al, 2018;Nathan & Contreras-Vidal, 2016;Nikolaev et al, 2016), improve training (Arico et al, 2018;Frolov et al, 2017;Gaur et al, 2018;Jayaram et al, 2016;Jeunet et al, 2016;Jiao et al, 2019;Mrachacz-Kersting et al, 2016;Paret et al, 2016;Tabar & Halici, 2017;Zhang et al, 2018), and reduce noise (Fuglsang et al, 2017;Gaur et al, 2018;Jiao et al, 2018;Kilicarslan et al, 2016;Maddirala & Shaik, 2016;Nakanishi et al, 2018;van Eyndhoven et al, 2017;Von Luhmann et al, 2017).…”

Section: Algorithm Typesmentioning

confidence: 99%

Recent advances and current trends in brain‐computer interface research and their applications

Žabčíková

Koudelková

Jašek

et al. 2022

Intl J of Devlp Neuroscience

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Brain‐computer interface (BCI) provides direct communication between the brain and an external device. BCI systems have become a trendy field of research in recent years. These systems can be used in a variety of applications to help both disabled and healthy people. Concerning significant BCI progress, we may assume that these systems are not very far from real‐world applications. This review has taken into account current trends in BCI research. In this survey, 100 most cited articles from the WOS database were selected over the last 4 years. This survey is divided into several sectors. These sectors are Medicine, Communication and Control, Entertainment, and Other BCI applications. The application area, recording method, signal acquisition types, and countries of origin have been identified in each article. This survey provides an overview of the BCI articles published from 2016 to 2020 and their current trends and advances in different application areas.

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Planning to revisit: neural activity in refixation precursors

Cited by 4 publications

References 136 publications

A passive BCI for monitoring the intentionality of the gaze-based moving object selection

A passive BCI for monitoring the intentionality of the gaze-based moving object selection

WildLab: A naturalistic free viewing experiment reveals previously unknown electroencephalography signatures of face processing

Recent advances and current trends in brain‐computer interface research and their applications

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