Emotion Recognition Using the Emotiv EPOC Device

Trung Duy PHAM, Dat Tran

Research output: A Conference proceeding or a Chapter in BookConference contribution

29 Citations (Scopus)

Abstract

Emotion plays an important role in the interaction between humans as emotion is fundamental to human experience, influencing cognition, perception, learning communication, and even rational decision-making. Therefore, studying emotion is indispensable. This paper aims at finding the relationships between EEG signals and human emotions based on emotion recognition experiments that are conducted using the commercial Emotiv EPOC headset to record EEG signals while participants are watching emotional movies. Alpha, beta, delta and theta bands filtered from the recorded EEG signals are used to train and evaluate classifiers with different learning techniques including Support Vector Machine, k-Nearest Neighbour, Naïve Bayes and AdaBoost.M1. Our experimental results show that we can use the Emotiv headset for emotion recognition and that the AdaBoost.M1 technique and the theta band provide the highest recognition rates.
Original languageEnglish
Title of host publicationInternational Conference on Neural Information Processing (ICONIP 2012)
Subtitle of host publicationLecture Notes in Computer Science
EditorsTingwen Huang, Zhigang Zeng, Chuandong Li, Chi Sing Leung
Place of PublicationGermany
PublisherSpringer
Pages394-399
Number of pages6
Volume7667
ISBN (Electronic)9783642345005
ISBN (Print)9783642344992
DOIs
Publication statusPublished - 2012
Event19th International Conference on Neural Information Processing 2012 - Doha, Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Conference

Conference19th International Conference on Neural Information Processing 2012
CountryQatar
CityDoha
Period12/11/1215/11/12

Fingerprint

Electroencephalography
Adaptive boosting
Support vector machines
Classifiers
Decision making
Communication
Experiments

Cite this

PHAM, T. D., & Tran, D. (2012). Emotion Recognition Using the Emotiv EPOC Device. In T. Huang, Z. Zeng, C. Li, & C. S. Leung (Eds.), International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science (Vol. 7667, pp. 394-399). Germany: Springer. https://doi.org/10.1007/978-3-642-34500-5_47
PHAM, Trung Duy ; Tran, Dat. / Emotion Recognition Using the Emotiv EPOC Device. International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. editor / Tingwen Huang ; Zhigang Zeng ; Chuandong Li ; Chi Sing Leung. Vol. 7667 Germany : Springer, 2012. pp. 394-399
@inproceedings{000908a6932145d7ad574d9ff1565e5c,
title = "Emotion Recognition Using the Emotiv EPOC Device",
abstract = "Emotion plays an important role in the interaction between humans as emotion is fundamental to human experience, influencing cognition, perception, learning communication, and even rational decision-making. Therefore, studying emotion is indispensable. This paper aims at finding the relationships between EEG signals and human emotions based on emotion recognition experiments that are conducted using the commercial Emotiv EPOC headset to record EEG signals while participants are watching emotional movies. Alpha, beta, delta and theta bands filtered from the recorded EEG signals are used to train and evaluate classifiers with different learning techniques including Support Vector Machine, k-Nearest Neighbour, Na{\"i}ve Bayes and AdaBoost.M1. Our experimental results show that we can use the Emotiv headset for emotion recognition and that the AdaBoost.M1 technique and the theta band provide the highest recognition rates.",
keywords = "Emotion Recognition",
author = "PHAM, {Trung Duy} and Dat Tran",
year = "2012",
doi = "10.1007/978-3-642-34500-5_47",
language = "English",
isbn = "9783642344992",
volume = "7667",
pages = "394--399",
editor = "Tingwen Huang and Zhigang Zeng and Chuandong Li and Leung, {Chi Sing}",
booktitle = "International Conference on Neural Information Processing (ICONIP 2012)",
publisher = "Springer",
address = "Netherlands",

}

PHAM, TD & Tran, D 2012, Emotion Recognition Using the Emotiv EPOC Device. in T Huang, Z Zeng, C Li & CS Leung (eds), International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. vol. 7667, Springer, Germany, pp. 394-399, 19th International Conference on Neural Information Processing 2012, Doha, Qatar, 12/11/12. https://doi.org/10.1007/978-3-642-34500-5_47

Emotion Recognition Using the Emotiv EPOC Device. / PHAM, Trung Duy; Tran, Dat.

International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. ed. / Tingwen Huang; Zhigang Zeng; Chuandong Li; Chi Sing Leung. Vol. 7667 Germany : Springer, 2012. p. 394-399.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Emotion Recognition Using the Emotiv EPOC Device

AU - PHAM, Trung Duy

AU - Tran, Dat

PY - 2012

Y1 - 2012

N2 - Emotion plays an important role in the interaction between humans as emotion is fundamental to human experience, influencing cognition, perception, learning communication, and even rational decision-making. Therefore, studying emotion is indispensable. This paper aims at finding the relationships between EEG signals and human emotions based on emotion recognition experiments that are conducted using the commercial Emotiv EPOC headset to record EEG signals while participants are watching emotional movies. Alpha, beta, delta and theta bands filtered from the recorded EEG signals are used to train and evaluate classifiers with different learning techniques including Support Vector Machine, k-Nearest Neighbour, Naïve Bayes and AdaBoost.M1. Our experimental results show that we can use the Emotiv headset for emotion recognition and that the AdaBoost.M1 technique and the theta band provide the highest recognition rates.

AB - Emotion plays an important role in the interaction between humans as emotion is fundamental to human experience, influencing cognition, perception, learning communication, and even rational decision-making. Therefore, studying emotion is indispensable. This paper aims at finding the relationships between EEG signals and human emotions based on emotion recognition experiments that are conducted using the commercial Emotiv EPOC headset to record EEG signals while participants are watching emotional movies. Alpha, beta, delta and theta bands filtered from the recorded EEG signals are used to train and evaluate classifiers with different learning techniques including Support Vector Machine, k-Nearest Neighbour, Naïve Bayes and AdaBoost.M1. Our experimental results show that we can use the Emotiv headset for emotion recognition and that the AdaBoost.M1 technique and the theta band provide the highest recognition rates.

KW - Emotion Recognition

U2 - 10.1007/978-3-642-34500-5_47

DO - 10.1007/978-3-642-34500-5_47

M3 - Conference contribution

SN - 9783642344992

VL - 7667

SP - 394

EP - 399

BT - International Conference on Neural Information Processing (ICONIP 2012)

A2 - Huang, Tingwen

A2 - Zeng, Zhigang

A2 - Li, Chuandong

A2 - Leung, Chi Sing

PB - Springer

CY - Germany

ER -

PHAM TD, Tran D. Emotion Recognition Using the Emotiv EPOC Device. In Huang T, Zeng Z, Li C, Leung CS, editors, International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. Vol. 7667. Germany: Springer. 2012. p. 394-399 https://doi.org/10.1007/978-3-642-34500-5_47