Multi channel brain EEG signals based emotional arousal classification with unsupervised feature learning using autoencoders

Deger Ayata; Yusuf Yaslan; Mustafa Kamasak

doi:10.1109/SIU.2017.7960629

Multi channel brain EEG signals based emotional arousal classification with unsupervised feature learning using autoencoders

Deger Ayata^*, Yusuf Yaslan, Mustafa Kamasak

^*Corresponding author for this work

Department of Computer Engineering

Istanbul Technical University

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

9 Citations (Scopus)

Abstract

The importance of learning important features in an automatic manner is growing exponentially as the volume of data and number of systems using pattern recognition techniques continue to increase. In this paper, arousal recognition from multi channels EEG signals was conducted using human crafted statistical features and learned features from 32 different EEG source channels. We have obtained 98.99% accuracy rate with unsupervised feature learning approach for Arousal classification. Unsupervised feature learning worked better compared to handcrafted feature approach.

Original language	English
Title of host publication	2017 25th Signal Processing and Communications Applications Conference, SIU 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509064946
DOIs	https://doi.org/10.1109/SIU.2017.7960629
Publication status	Published - 27 Jun 2017
Event	25th Signal Processing and Communications Applications Conference, SIU 2017 - Antalya, Turkey Duration: 15 May 2017 → 18 May 2017

Publication series

Name	2017 25th Signal Processing and Communications Applications Conference, SIU 2017

Conference

Conference	25th Signal Processing and Communications Applications Conference, SIU 2017
Country/Territory	Turkey
City	Antalya
Period	15/05/17 → 18/05/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Arousal Recognition
Autoencoders
Brain Computer Interface
Brain EEG Analysis
Feature Learning
kNN
Multi-channel sensor processing
Random Forests
Signal Processing

Access to Document

10.1109/SIU.2017.7960629

Cite this

Ayata, D., Yaslan, Y., & Kamasak, M. (2017). Multi channel brain EEG signals based emotional arousal classification with unsupervised feature learning using autoencoders. In 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 Article 7960629 (2017 25th Signal Processing and Communications Applications Conference, SIU 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2017.7960629

Ayata, Deger ; Yaslan, Yusuf ; Kamasak, Mustafa. / Multi channel brain EEG signals based emotional arousal classification with unsupervised feature learning using autoencoders. 2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc., 2017. (2017 25th Signal Processing and Communications Applications Conference, SIU 2017).

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title = "Multi channel brain EEG signals based emotional arousal classification with unsupervised feature learning using autoencoders",

abstract = "The importance of learning important features in an automatic manner is growing exponentially as the volume of data and number of systems using pattern recognition techniques continue to increase. In this paper, arousal recognition from multi channels EEG signals was conducted using human crafted statistical features and learned features from 32 different EEG source channels. We have obtained 98.99% accuracy rate with unsupervised feature learning approach for Arousal classification. Unsupervised feature learning worked better compared to handcrafted feature approach.",

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author = "Deger Ayata and Yusuf Yaslan and Mustafa Kamasak",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 25th Signal Processing and Communications Applications Conference, SIU 2017 ; Conference date: 15-05-2017 Through 18-05-2017",

year = "2017",

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day = "27",

doi = "10.1109/SIU.2017.7960629",

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Ayata, D, Yaslan, Y & Kamasak, M 2017, Multi channel brain EEG signals based emotional arousal classification with unsupervised feature learning using autoencoders. in 2017 25th Signal Processing and Communications Applications Conference, SIU 2017., 7960629, 2017 25th Signal Processing and Communications Applications Conference, SIU 2017, Institute of Electrical and Electronics Engineers Inc., 25th Signal Processing and Communications Applications Conference, SIU 2017, Antalya, Turkey, 15/05/17. https://doi.org/10.1109/SIU.2017.7960629

Multi channel brain EEG signals based emotional arousal classification with unsupervised feature learning using autoencoders. / Ayata, Deger; Yaslan, Yusuf ; Kamasak, Mustafa.
2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7960629 (2017 25th Signal Processing and Communications Applications Conference, SIU 2017).

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

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AB - The importance of learning important features in an automatic manner is growing exponentially as the volume of data and number of systems using pattern recognition techniques continue to increase. In this paper, arousal recognition from multi channels EEG signals was conducted using human crafted statistical features and learned features from 32 different EEG source channels. We have obtained 98.99% accuracy rate with unsupervised feature learning approach for Arousal classification. Unsupervised feature learning worked better compared to handcrafted feature approach.

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M3 - Conference contribution

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Ayata D, Yaslan Y , Kamasak M. Multi channel brain EEG signals based emotional arousal classification with unsupervised feature learning using autoencoders. In 2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 7960629. (2017 25th Signal Processing and Communications Applications Conference, SIU 2017). doi: 10.1109/SIU.2017.7960629

Multi channel brain EEG signals based emotional arousal classification with unsupervised feature learning using autoencoders

Abstract

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Bibliographical note

Keywords

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