EEG analysis of implicit human visual perception

Mustafa, Maryam; Lindemann, Lea; Magnor, Marcus

doi:10.1145/2207676.2207746

Cited by 20 publications

(13 citation statements)

References 12 publications

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“…For the EEG data, ERPs were extracted as well as performing frequency analysis. For analysis of the ERPs [35], the data was extracted around the onset of the distortion. It could be shown that for artifacts that are on the moving part of the video (i.e.…”

Section: Spectral Analysismentioning

confidence: 99%

Review on using physiology in quality of experience

Arndt¹,

Brunnström²,

Cheng³

et al. 2016

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In the area of Quality of Experience (QoE), one challenge is to design test methodologies in order to evaluate the perceived quality of multimedia content delivered through technical systems. Traditionally, this evaluation is done using subjective opinion tests. However, sometimes it is difficult for observers to communicate the experienced quality through the given scale. Furthermore, those tests do not give insights into how the user is reacting on an internal physiological level. To overcome these issues, one approach is to use physiological measures, in order to derive a direct non-verbal response of the recipient. In this paper, we review studies that have been performed in the domain of QoE using physiological measures and we look into current activities in standardization bodies. We present challenges this research faces, and give an overview on what researchers should be aware of when they want to start working in this research area.

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Section: Spectral Analysismentioning

confidence: 99%

Review on using physiology in quality of experience

Arndt¹,

Brunnström²,

Cheng³

et al. 2016

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“…This body of work is particularly novel since, typically, EEG has not been used for complex or moving stimuli. In a 2012 study, we explored the possibility of detecting artifacts in video sequences with EEG [11]. The study measured the covert (implicit) visual processing associated with different types of artifacts.…”

Section: Video and Image Quality Assessment With Eegmentioning

confidence: 99%

ElectroEncephaloGraphics: Making Waves in Computer Graphics Research

Mustafa

Magnor

2014

IEEE Comput. Grap. Appl.

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We present the application of ElectroEncephaloGraphy (EEG) as a novel modality for investigating perceptual graphics problems. Until recently, EEG has predominantly been used for clinical diagnosis, in psychology and by the BCI community. Here we want to extend its scope to assist in understanding the perception of visual output from graphics applications and to create new approaches based on direct neural feedback. We begin by introducing the fundamentals of EEG measurements, its neurophysiological basis and the limitations of EEG in determining certain aspects of visual perception. We then present three different areas for EEG application in graphics: determining perceived image and video quality through the detection of typical rendering artifacts, evaluating visualization effectiveness by calculating cognitive load from EEG data, and automatic optimization of rendering parameters for images and videos based on implicit neural feedback. We conclude with an outlook on what the future of EEG in graphics may hold.

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“…The most relevant research has been done by Mustafa et al [2012] who looked at artifacts in videos and the corresponding EEG results. They showed the obvious brain response to artifacts in videos in the form of Event-Related Potentials (ERP's).…”

Section: Perception and Eegmentioning

confidence: 99%

“…Despite a substantial amount of research in applying perception to graphics and IBR [Vangorp et al 2011;Ferwerda et al 1996;Anderson et al 2011], only recently has there been a growing interest in using an EEG to analyze visual processing [Mustafa et al 2012;Lindemann and Magnor 2011;Rossion and Caharel 2011;Agam and Sekuler 2007]. Also, the use of an EEG in computer graphics research is now much easier with the advent of systems like the Emotiv EPOC neuroheadset [Emotiv 2012].…”

Section: Introductionmentioning

confidence: 99%

“…The two most important things are plausibility and absence of objectionable artifacts. In our earlier work [Mustafa et al 2012] we looked into how the HVS reacts to artifacts in an image sequence but our analysis was limited only to ERP's which meant looking at averaged data only without attempting to classify single trials based on artifact presence. In this article, we use a wavelet-based algorithm for the classification of EEG signals for different artifacts versus groundtruth sequences and analyze the overall emotional response to the video.…”

Section: Introductionmentioning

confidence: 99%

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Single-trial EEG classification of artifacts in videos

Mustafa

Guthe

Magnor

2012

ACM Trans. Appl. Percept.

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In this article we use an ElectroEncephaloGraph (EEG) to explore the perception of artifacts that typically appear during rendering and determine the perceptual quality of a sequence of images. Although there is an emerging interest in using an EEG for image quality assessment, one of the main impediments to the use of an EEG is the very low Signal-to-Noise Ratio (SNR) which makes it exceedingly difficult to distinguish neural responses from noise. Traditionally, event-related potentials have been used for analysis of EEG data. However, they rely on averaging and so require a large number of participants and trials to get meaningful data. Also, due the the low SNR ERP's are not suited for single-trial classification.We propose a novel wavelet-based approach for evaluating EEG signals which allows us to predict the perceived image quality from only a single trial. Our wavelet-based algorithm is able to filter the EEG data and remove noise, eliminating the need for many participants or many trials. With this approach it is possible to use data from only 10 electrode channels for single-trial classification and predict the presence of an artifact with an accuracy of 85%. We also show that it is possible to differentiate and classify a trial based on the exact type of artifact viewed. Our work is particularly useful for understanding how the human visual system responds to different types of degradations in images and videos. An understanding of the perception of typical image-based rendering artifacts forms the basis for the optimization of rendering and masking algorithms.

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EEG analysis of implicit human visual perception

Cited by 20 publications

References 12 publications

Review on using physiology in quality of experience

Review on using physiology in quality of experience

ElectroEncephaloGraphics: Making Waves in Computer Graphics Research

Single-trial EEG classification of artifacts in videos

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