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

^*Bu çalışma için yazışmadan sorumlu yazar

Bilgisayar Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

9 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2017 25th Signal Processing and Communications Applications Conference, SIU 2017
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781509064946
DOI'lar	https://doi.org/10.1109/SIU.2017.7960629
Yayın durumu	Yayınlandı - 27 Haz 2017
Etkinlik	25th Signal Processing and Communications Applications Conference, SIU 2017 - Antalya, Türkiye Süre: 15 May 2017 → 18 May 2017

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	25th Signal Processing and Communications Applications Conference, SIU 2017
Ülke/Bölge	Türkiye
Şehir	Antalya
Periyot	15/05/17 → 18/05/17

Bibliyografik not

Publisher Copyright:
© 2017 IEEE.

Belgeye Erişim

10.1109/SIU.2017.7960629

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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 Makale 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).

@inproceedings{88f1a51f85d64066a9b3f50cea8c30c9,

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.",

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

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",

month = jun,

day = "27",

doi = "10.1109/SIU.2017.7960629",

language = "English",

series = "2017 25th Signal Processing and Communications Applications Conference, SIU 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2017 25th Signal Processing and Communications Applications Conference, SIU 2017",

address = "United States",

}

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, Türkiye, 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).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Multi channel brain EEG signals based emotional arousal classification with unsupervised feature learning using autoencoders

AU - Ayata, Deger

AU - Yaslan, Yusuf

AU - Kamasak, Mustafa

PY - 2017/6/27

Y1 - 2017/6/27

N2 - 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.

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.

KW - Arousal Recognition

KW - Autoencoders

KW - Brain Computer Interface

KW - Brain EEG Analysis

KW - Feature Learning

KW - kNN

KW - Multi-channel sensor processing

KW - Random Forests

KW - Signal Processing

UR - https://www.scopus.com/pages/publications/85026328523

U2 - 10.1109/SIU.2017.7960629

DO - 10.1109/SIU.2017.7960629

M3 - Conference contribution

AN - SCOPUS:85026328523

T3 - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

BT - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 25th Signal Processing and Communications Applications Conference, SIU 2017

Y2 - 15 May 2017 through 18 May 2017

ER -

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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap