Implicit affective profiling of subjects based on physiological data coupling

Kroupi, Eleni; Vesin, Jean‐Marc; Ebrahimi, Touradj

doi:10.1080/2326263x.2014.912882

Cited by 8 publications

(21 citation statements)

References 56 publications

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“…Phase-Amplitude coupling represents one type of interaction and typically refers to modulation of the amplitude of high-frequency oscillators by the phase of low-frequency ones ( Samiee et al ). Typically, such phase-amplitude coupling measures are computed from EEG signals alone (Schutter and Knyazev, 2012 ), but the concept of electrodermal activity phase coupled to EEG amplitude was recently introduced as a correlate of emotion, particularly for high arousing, very pleasant and very unpleasant stimuli (Kroupi et al, 2013 , 2014 ). Here, we test three different GSR-phase and EEG-amplitude coupling measures.…”

Section: Methodsmentioning

confidence: 99%

“…Here, we test three different GSR-phase and EEG-amplitude coupling measures. For the sake of notation, assume u ( n ) is the rapid transient response called skin conductance response (SCR) with a narrowband of 0.5–1Hz (Kroupi et al, 2014 ), of the time-domain GSR signal. Using the Hilbert transform (Gabor, 1946 ), we can extract the signal's instantaneous phase ϕ u ( n ) as in Kroupi et al ( 2014 ):…”

Section: Methodsmentioning

confidence: 99%

“…For the amplitude envelope of the EEG signal ( A ( s k ( n ))), a shape-preserving piecewise cubic interpolation method of neighboring values is used, as in Kroupi et al ( 2014 ). Given the GSR signal and phase, as well as the EEG amplitude envelope signals, the following coupling measures were computed.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, in addition to EEG correlates, affective state information has been widely obtained from physiological signals measured from the peripheral autonomic nervous system (PANS) (Nasoz et al, 2003 ; Lisetti and Nasoz, 2004 ; Wu and Parsons, 2011 ), particularly the galvanic skin response (GSR), a measure of the amount of sweat (conductivity) in the skin (Picard and Healey, 1997 ; Bersak et al, 2001 ). More recently, the interaction between the PANS and central nervous systems (CNS) was measured via a phase-amplitude coupling (PAC) between GSR and EEG signals and promising emotion recognition results were found for highly arousing videos (Kroupi et al, 2014 ). As emphasized in Canolty et al ( 2012 ), however, different ways of computing PAC may lead to complementary information.…”

Section: Introductionmentioning

confidence: 99%

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Electroencephalography Amplitude Modulation Analysis for Automated Affective Tagging of Music Video Clips

Clerico

Tiwari

Gupta

et al. 2018

Front. Comput. Neurosci.

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The quantity of music content is rapidly increasing and automated affective tagging of music video clips can enable the development of intelligent retrieval, music recommendation, automatic playlist generators, and music browsing interfaces tuned to the users' current desires, preferences, or affective states. To achieve this goal, the field of affective computing has emerged, in particular the development of so-called affective brain-computer interfaces, which measure the user's affective state directly from measured brain waves using non-invasive tools, such as electroencephalography (EEG). Typically, conventional features extracted from the EEG signal have been used, such as frequency subband powers and/or inter-hemispheric power asymmetry indices. More recently, the coupling between EEG and peripheral physiological signals, such as the galvanic skin response (GSR), have also been proposed. Here, we show the importance of EEG amplitude modulations and propose several new features that measure the amplitude-amplitude cross-frequency coupling per EEG electrode, as well as linear and non-linear connections between multiple electrode pairs. When tested on a publicly available dataset of music video clips tagged with subjective affective ratings, support vector classifiers trained on the proposed features were shown to outperform those trained on conventional benchmark EEG features by as much as 6, 20, 8, and 7% for arousal, valence, dominance and liking, respectively. Moreover, fusion of the proposed features with EEG-GSR coupling features showed to be particularly useful for arousal (feature-level fusion) and liking (decision-level fusion) prediction. Together, these findings show the importance of the proposed features to characterize human affective states during music clip watching.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Electroencephalography Amplitude Modulation Analysis for Automated Affective Tagging of Music Video Clips

Clerico

Tiwari

Gupta

et al. 2018

Front. Comput. Neurosci.

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“…Compromises of the two approaches are also possible. For instance, a concept of weak synchronization, called phaseamplitude coupling, was introduced to integrate cerebral and peripheral features (i.e., EEG and GSR) in [27]. Phaseamplitude coupling is a measure of the interaction between two signals having different frequencies, i.e., modulation of the amplitude of the higher frequency signal by the phase of the lower frequency signal.…”

Section: F Integration Of Brain and Peripheral Physiological Signalsmentioning

confidence: 99%

Implicit Analysis of Perceptual Multimedia Experience Based on Physiological Response: A Review

Moon¹,

Lee²

2017

IEEE Trans. Multimedia

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The exponential growth of popularity of multimedia has led needs for user-centric adaptive applications that manage multimedia content more effectively. Implicit analysis, which examines users' perceptual experience of multimedia by monitoring physiological or behavioral cues, has potential to satisfy such demands. Particularly, physiological signals categorized into cerebral physiological signals (electroencephalography, functional magnetic resonance imaging, and functional nearinfrared spectroscopy) and peripheral physiological signals (heart rate, respiration, skin temperature, etc.) have recently received attention along with notable development of wearable physiological sensors. In this paper, we review existing studies on physiological signal analysis exploring perceptual experience of multimedia. Furthermore, we discuss current trends and challenges.Index Terms-physiological signal, perceptual experience, implicit analysis, multimedia 1 Overviews of implicit measurement techniques using behavioral cues can be found in [6], [7].This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication.The final version of record is available at http://dx.

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Cultural communication in double-layer coupling social network based on association rules in big data

2019

Pers Ubiquit Comput

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Implicit affective profiling of subjects based on physiological data coupling

Cited by 8 publications

References 56 publications

Electroencephalography Amplitude Modulation Analysis for Automated Affective Tagging of Music Video Clips

Electroencephalography Amplitude Modulation Analysis for Automated Affective Tagging of Music Video Clips

Implicit Analysis of Perceptual Multimedia Experience Based on Physiological Response: A Review

Cultural communication in double-layer coupling social network based on association rules in big data

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