The Impact of Task Demands on Fixation-Related Brain Potentials during Guided Search

Ries, Anthony J.; Touryan, Jon; Ahrens, Barry; Connolly, Patrick

doi:10.1371/journal.pone.0157260

Cited by 40 publications

(36 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Preferably, this would be in an environment with minimal noise affecting the brain and eye signals, but also situations where the signal is strong—e.g., low workload scenarios are beneficial when exploiting the P300 signal to distinguish between targets and non-targets (current study, Thurlings et al, 2013; Ries et al, 2016). An example that may capture these elements is robust image triage.…”

Section: Discussionmentioning

confidence: 89%

EEG and Eye Tracking Signatures of Target Encoding during Structured Visual Search

Brouwer

Hogervorst

Oudejans

et al. 2017

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

EEG and eye tracking variables are potential sources of information about the underlying processes of target detection and storage during visual search. Fixation duration, pupil size and event related potentials (ERPs) locked to the onset of fixation or saccade (saccade-related potentials, SRPs) have been reported to differ dependent on whether a target or a non-target is currently fixated. Here we focus on the question of whether these variables also differ between targets that are subsequently reported (hits) and targets that are not (misses). Observers were asked to scan 15 locations that were consecutively highlighted for 1 s in pseudo-random order. Highlighted locations displayed either a target or a non-target stimulus with two, three or four targets per trial. After scanning, participants indicated which locations had displayed a target. To induce memory encoding failures, participants concurrently performed an aurally presented math task (high load condition). In a low load condition, participants ignored the math task. As expected, more targets were missed in the high compared with the low load condition. For both conditions, eye tracking features distinguished better between hits and misses than between targets and non-targets (with larger pupil size and shorter fixations for missed compared with correctly encoded targets). In contrast, SRP features distinguished better between targets and non-targets than between hits and misses (with average SRPs showing larger P300 waveforms for targets than for non-targets). Single trial classification results were consistent with these averages. This work suggests complementary contributions of eye and EEG measures in potential applications to support search and detect tasks. SRPs may be useful to monitor what objects are relevant to an observer, and eye variables may indicate whether the observer should be reminded of them later.

show abstract

Section: Discussionmentioning

confidence: 89%

EEG and Eye Tracking Signatures of Target Encoding during Structured Visual Search

Brouwer

Hogervorst

Oudejans

et al. 2017

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The results showed decreased P300 amplitude with increased working memory load for the incongruent flanker stimuli. P300 latency is also affected by task demands, as demonstrated in a recent study by Ries et al [35], which showed that target response latency significantly increased as a function of auditory working memory load.…”

Section: Task Demandsmentioning

confidence: 79%

“…Of course, the decision to reject the Null Hypothesis (i.e that target amplitudes are the same in both conditions) is a function of several factors, including significance threshold and multiple-comparison corrections. In the original paper [35], the authors did not report a significant difference in amplitudes after performing a multiplecomparisons correction. Use of CNN outputs would have affected this result due to the enhanced SNR of the CNN method.…”

Section: Noise Resistancementioning

confidence: 99%

See 1 more Smart Citation

Decoding P300 Variability using Convolutional Neural Networks

Solon

Lawhern

Touryan

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Deep convolutional neural networks (CNN) have previously been shown to be useful tools for signal decoding and analysis in a variety of complex domains, such as image processing and speech recognition. By learning from large amounts of data, the representations encoded by these deep networks are often invariant to moderate changes in the underlying feature spaces. Recently, we proposed a CNN architecture that could be applied to electroencephalogram (EEG) decoding and analysis. In this article, we train our CNN model using data from prior experiments in order to later decode the P300 evoked response from an unseen, hold-out experiment. We analyze the CNN output as a function of the underlying variability in the P300 response and demonstrate that the CNN output is sensitive to the experiment-induced changes in the neural response. We then assess the utility of our approach as a means of improving the overall signalto-noise ratio in the EEG record. Finally, we show an example of how CNN-based decoding can be applied to the analysis of complex data.

show abstract

“…Dimigen, Sommer, Hohlfeld, Jacobs, & Kliegl, 2011). Most studies so far have studied the EEG in the time domain, that is, fixation-related potentials (FRPs), for example in reading research (Dimigen, et al, 2011;Frey, Lemaire, Vercueil, & Guérin-Dugué, 2018;Henderson, Luke, Schmidt, & Richards, 2013;Hutzler et al, 2007;Kliegl, Dambacher, Dimigen, & Sommer, 2014;Kornrumpf, Dimigen, & Sommer, 2017;Kornrumpf, Niefind, Sommer, & Dimigen, 2016;Léger et al, 2014;Weiss, Knakker, & Vidnyánszky, 2016), natural scene perception (Giannini, Alexander, Nikolaev, & van Leeuwen, 2018;Simola, Le Fevre, Torniainen, & Baccino, 2015;Simola, Torniainen, Moisala, Kivikangas, & Krause, 2013), visual search (Brouwer, Hogervorst, Oudejans, Ries, & Touryan, 2017;Kamienkowski, Ison, Quiroga, & Sigman, 2012;Kaunitz et al, 2014;Ries, Touryan, Ahrens, & Connolly, 2016;Winslow et al, 2010), decision making (Frey et al, 2013), and human-computer interaction (Léger et al, 2014). Despite methodological challenges (see below) these studies concurrently report FRP-effects comparable to classical ERP-effects (e.g., N400-like effects for word predictability; Dimigen et al, 2011), thus validating the feasibility and meaningfulness of fixation-related EEG data analyses.…”

Section: Fixation-related Eeg Data Analysismentioning

confidence: 99%

Fixation-related EEG frequency band power analysis

Scharinger

2018

FLR

View full text Add to dashboard Cite

During the last decade the combined recording of eye-tracking data and electroencephalographic (EEG) data has led to the methodology of fixation-related potentials analysis (FRP). This methodology has been increasingly and successfully used to study EEG correlates in the time domain (i.e., event-related potentials, ERPs) of cognitive processing in free viewing situations like text reading or natural scene perception. Basically, fixation-onset serves as time-locking event for epoching and analysing the EEG data. In this article the methodology of fixation-related frequency band power analysis (FRBP) is proposed and conceptually outlined to study cognitive load and affective variations in learners during free viewing situations of multimedia learning materials (i.e., combinations of textual and pictorial elements). The EEG alpha frequency band power at parietal electrodes may serve as a valid measure of cognitive load, whereas the frontal alpha asymmetry may serve as a measure of affective variations. I will introduce and motivate the measures and the methodology and discuss methodological challenges and potential ways to overcome them. The methodology is frontline for learning research, first, as to date the EEG has been rarely used to study design effects of multimedia learning materials and second, as fixation-related EEG data analysis has rarely been done focussing on the frequency domain (i.e., FRBP). Despite methodological challenges still to be solved, FRBP may provide a more indepth picture of cognitive processing during multimedia learning compared to eyetracking data or EEG data in isolation and thus may help clarifying effects of multimedia design decisions.

show abstract

The Impact of Task Demands on Fixation-Related Brain Potentials during Guided Search

Cited by 40 publications

References 53 publications

EEG and Eye Tracking Signatures of Target Encoding during Structured Visual Search

EEG and Eye Tracking Signatures of Target Encoding during Structured Visual Search

Decoding P300 Variability using Convolutional Neural Networks

Fixation-related EEG frequency band power analysis

Contact Info

Product

Resources

About