Decoding affect in videos employing the MEG brain signal

Mojtaba Khomami Abadi; Mostafa Kia; Ramanathan Subramanian; Paolo Avesani; Nicu Sebe

doi:10.1109/FG.2013.6553809

Decoding affect in videos employing the MEG brain signal

Mojtaba Khomami Abadi
, Mostafa Kia
, Ramanathan Subramanian
, Paolo Avesani
, Nicu Sebe

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

This paper presents characterization of affect (valence and arousal) using the Magnetoencephalogram (MEG) brain signal. We attempt single-trial classification of movie and music videos with MEG responses extracted from seven participants. The main findings of this study are that: (i) the MEG signal effectively encodes affective viewer responses, (ii) clip arousal is better predicted than valence employing MEG and (iii) prediction performance is better for movie clips as compared to music videos.

Original language	English
Title of host publication	2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013
Editors	Rama Chellappa, Xilin Chen, Qiang Ji, Maja Pantic, Stan Sclaroff, Lijun Yin
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-6
Number of pages	6
ISBN (Print)	9781467355452
DOIs	https://doi.org/10.1109/FG.2013.6553809
Publication status	Published - 2013
Externally published	Yes
Event	2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013 - Shanghai, China Duration: 22 Apr 2013 → 26 Apr 2013

Publication series

Name	2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013

Conference

Conference	2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013
Country/Territory	China
City	Shanghai
Period	22/04/13 → 26/04/13

Access to Document

10.1109/FG.2013.6553809

Cite this

Abadi, M. K., Kia, M., Subramanian, R., Avesani, P., & Sebe, N. (2013). Decoding affect in videos employing the MEG brain signal. In R. Chellappa, X. Chen, Q. Ji, M. Pantic, S. Sclaroff, & L. Yin (Eds.), 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013 (pp. 1-6). Article 6553809 (2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/FG.2013.6553809

Abadi, Mojtaba Khomami ; Kia, Mostafa ; Subramanian, Ramanathan et al. / Decoding affect in videos employing the MEG brain signal. 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013. editor / Rama Chellappa ; Xilin Chen ; Qiang Ji ; Maja Pantic ; Stan Sclaroff ; Lijun Yin. United States : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 1-6 (2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013).

@inproceedings{e32daced8f804510b960d78b57ce9791,

title = "Decoding affect in videos employing the MEG brain signal",

abstract = "This paper presents characterization of affect (valence and arousal) using the Magnetoencephalogram (MEG) brain signal. We attempt single-trial classification of movie and music videos with MEG responses extracted from seven participants. The main findings of this study are that: (i) the MEG signal effectively encodes affective viewer responses, (ii) clip arousal is better predicted than valence employing MEG and (iii) prediction performance is better for movie clips as compared to music videos.",

author = "Abadi, \{Mojtaba Khomami\} and Mostafa Kia and Ramanathan Subramanian and Paolo Avesani and Nicu Sebe",

year = "2013",

doi = "10.1109/FG.2013.6553809",

language = "English",

isbn = "9781467355452",

series = "2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "1--6",

editor = "Rama Chellappa and Xilin Chen and Qiang Ji and Maja Pantic and Stan Sclaroff and Lijun Yin",

booktitle = "2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013",

address = "United States",

note = "2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013 ; Conference date: 22-04-2013 Through 26-04-2013",

}

Abadi, MK, Kia, M, Subramanian, R, Avesani, P & Sebe, N 2013, Decoding affect in videos employing the MEG brain signal. in R Chellappa, X Chen, Q Ji, M Pantic, S Sclaroff & L Yin (eds), 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013., 6553809, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 1-6, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013, Shanghai, China, 22/04/13. https://doi.org/10.1109/FG.2013.6553809

Decoding affect in videos employing the MEG brain signal. / Abadi, Mojtaba Khomami; Kia, Mostafa; Subramanian, Ramanathan et al.
2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013. ed. / Rama Chellappa; Xilin Chen; Qiang Ji; Maja Pantic; Stan Sclaroff; Lijun Yin. United States: IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 1-6 6553809 (2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Decoding affect in videos employing the MEG brain signal

AU - Abadi, Mojtaba Khomami

AU - Kia, Mostafa

AU - Subramanian, Ramanathan

AU - Avesani, Paolo

AU - Sebe, Nicu

PY - 2013

Y1 - 2013

N2 - This paper presents characterization of affect (valence and arousal) using the Magnetoencephalogram (MEG) brain signal. We attempt single-trial classification of movie and music videos with MEG responses extracted from seven participants. The main findings of this study are that: (i) the MEG signal effectively encodes affective viewer responses, (ii) clip arousal is better predicted than valence employing MEG and (iii) prediction performance is better for movie clips as compared to music videos.

AB - This paper presents characterization of affect (valence and arousal) using the Magnetoencephalogram (MEG) brain signal. We attempt single-trial classification of movie and music videos with MEG responses extracted from seven participants. The main findings of this study are that: (i) the MEG signal effectively encodes affective viewer responses, (ii) clip arousal is better predicted than valence employing MEG and (iii) prediction performance is better for movie clips as compared to music videos.

UR - https://www.scopus.com/pages/publications/84881487484

U2 - 10.1109/FG.2013.6553809

DO - 10.1109/FG.2013.6553809

M3 - Conference contribution

AN - SCOPUS:84881487484

SN - 9781467355452

T3 - 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013

SP - 1

EP - 6

BT - 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013

A2 - Chellappa, Rama

A2 - Chen, Xilin

A2 - Ji, Qiang

A2 - Pantic, Maja

A2 - Sclaroff, Stan

A2 - Yin, Lijun

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - United States

T2 - 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013

Y2 - 22 April 2013 through 26 April 2013

ER -

Abadi MK, Kia M, Subramanian R, Avesani P, Sebe N. Decoding affect in videos employing the MEG brain signal. In Chellappa R, Chen X, Ji Q, Pantic M, Sclaroff S, Yin L, editors, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013. United States: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 1-6. 6553809. (2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013). doi: 10.1109/FG.2013.6553809

Decoding affect in videos employing the MEG brain signal

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