Decoding affect in videos employing the MEG brain signal

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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	Proceedings of IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013
Place of Publication	USA
Publisher	IEEE Computer Society
Pages	1-6
Number of pages	6
ISBN (Print)	978-1-4673-5545-2
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) - Shanghai, China Duration: 22 Apr 2023 → 26 Apr 2024 Conference number: 10 https://www.computer.org/csdl/proceedings/fg/2013/12OmNzFMFow

Conference

Conference	2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)
Country/Territory	China
City	Shanghai
Period	22/04/23 → 26/04/24
Internet address	https://www.computer.org/csdl/proceedings/fg/2013/12OmNzFMFow

Keywords

EWI-24277
HMI-HF: Human Factors
particle filtering (numerical methods)
pose estimation
target tracking

Access to Document

10.1109/FG.2013.6553809Licence: Taverne

Cite this

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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.",

keywords = "EWI-24277, HMI-HF: Human Factors, particle filtering (numerical methods), pose estimation, target tracking",

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

note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) ; Conference date: 22-04-2023 Through 26-04-2024",

year = "2013",

doi = "10.1109/FG.2013.6553809",

language = "English",

isbn = "978-1-4673-5545-2",

pages = "1--6",

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

publisher = "IEEE Computer Society",

address = "United States",

url = "https://www.computer.org/csdl/proceedings/fg/2013/12OmNzFMFow",

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Abadi, MK, Kia, SM, Subramanian, R, Avesani, P & Sebe, N 2013, Decoding affect in videos employing the MEG brain signal. in Proceedings of IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013. IEEE Computer Society, USA, pp. 1-6, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Shanghai, China, 22/04/23. https://doi.org/10.1109/FG.2013.6553809

Decoding affect in videos employing the MEG brain signal. / Abadi, Mojtaba Khomami; Kia, Seyed Mostafa; Subramanian, Ramanathan et al.
Proceedings of IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013. USA: IEEE Computer Society, 2013. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Decoding affect in videos employing the MEG brain signal

AU - Abadi, Mojtaba Khomami

AU - Kia, Seyed Mostafa

AU - Subramanian, Ramanathan

AU - Avesani, Paolo

AU - Sebe, Nicu

N1 - Conference code: 10

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.

KW - EWI-24277

KW - HMI-HF: Human Factors

KW - particle filtering (numerical methods)

KW - pose estimation

KW - target tracking

U2 - 10.1109/FG.2013.6553809

DO - 10.1109/FG.2013.6553809

M3 - Conference contribution

SN - 978-1-4673-5545-2

SP - 1

EP - 6

BT - Proceedings of IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013

PB - IEEE Computer Society

CY - USA

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

Y2 - 22 April 2023 through 26 April 2024

ER -

Decoding affect in videos employing the MEG brain signal

Abstract

Conference

Keywords

Access to Document

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