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Pozitif ve Negatif Duyguların Ayrımında Etkili EEG Kanallarının Dalgacık Dönüşümü ve Destek Vektör Makineleri ile Belirlenmesi

Year 2019, Volume: 12 Issue: 3, 229 - 237, 31.07.2019
https://doi.org/10.17671/gazibtd.482939

Abstract

Duygular
kişilerin yaşamlarını ve karar verme mekanizmalarını hayatının tamamında
etkilemektedir. İnsanlar duygulara kelimeleri, sesleri, yüz mimiklerini ve
vücut dillerini kullanarak istemli ya istemsiz bir şekilde, iş yaparken,
gözlemlerken, düşünürken kısacası çevresiyle iletişim kurarken başvururlar.
Bundan dolayı, duyguların davranışlarını analiz etmek ve anlamak büyük önem arz
etmektedir. Beyin sinyallerine dayalı gerçekleştirilen duygu tahmini günümüzde
Beyin-Bilgisayar Arayüzü (BBA) uygulamalarında büyük yarar sağlamaktadır. BBA
uygulamaları daha çok sağlık, eğitim, güvenlik, sanal gerçeklik, bilgisayar
oyunları olmak üzere birbirinden farklı birçok alanda kullanılmaktadır. Ancak,
beyin sinyallerinin elde edilmesi sırasında gürültülerin ortaya çıkması, EEG
kanallarının yanlış seçilmesi, verilerin yoğun olması ve uygun olmayan özellik
çıkarım yöntemlerinin kullanılması, BBA uygulamalarının yeterli seviyeye
gelememelerine neden olmaktadır. Bu çalışmada, hangi EEG kanallarının
pozitif-negatif duyguların ayrımında etkili olduğu belirlenmeye çalışılmış ve
DEAP veri setindeki 32 kanallı EEG sinyalleri kullanılmıştır. Özellik çıkarım aşamasında,
dalgacık dönüşümü, bilgi ölçüm yöntemleri ve istatistiksel yöntemler
kullanılarak etkili EEG kanallarının belirlenmesi hedeflenmiştir. Çalışmanın
son aşamasında ise, elde edilen özelliklerden yola çıkılarak oluşturulan eğitim
kümesi DVM (Destek Vektör Makineleri) kullanılarak sınıflandırılmıştır.
Önerilen yöntemin sınıflandırma performansı, sınıflandırma kesinliği, log-kaybı
ve on kat çapraz-doğrulama) ile belirlenmiştir. Her bir EEG kanalı için
doğruluk oranı hesaplanmış ve ortalama başarım %74 olacak şekilde
gözlemlenmiştir. Önerilen yöntem ve tekniklere göre en etkili EEG kanalları
Fp1, FC6, C4, CP1, CP5, CP6, T7, P7 ve Pz olarak belirlenmiştir.

References

  • M. Naji, M. Firoozabadi, and P. Azadfallah, ‘’Emotion Classification During Music Listening from Forehead Biosignals’’, Signal, Image and Video Processing, 9(6), 1365-1375, 2015.
  • A. Turnip, A. I. Simbolon, M. F. Amri, P. Sihombing, R. H. Setiadi., and E. Mulyana, ‘’Backpropagation Neural Networks Training for EEG-SSVEP Classification of Emotion Recognition’’, Internetworking Indenosia Journal, 9(1), 53-57, 2017
  • S. J. Westerman, P. H. Gardner, and E. J. Sutherland, ‘’Usability Testing Emotion-Orianted Computing Systems: Psychometric Assessment’’, HUMAINE Deliverable, 1-53, 2006.
  • M. Lhommet, and S. C. Marsella, ‘’Expressing Emotion through Posture and Gesture’’, The Oxford Handbook of Affective Computing, Oxford Library of Psychology, Oxford, 2015.
  • T. B. Alakuş, and İ. Türoğlu, ‘’EEG Based Emotion Analysis Systems’’, Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 11(1), 26-39, 2018.
  • W. Szwoch, ‘’Using Physiological Signals for Emotion’’, Sopot, Poland, 2013.
  • J. Pan., Y. Li., and J. Wang, ‘’An EEG-Based Brain-Computer Interface for Emotion Recognition’’, 2016 International Joint Conference on Neural Networks, Canada, 2063-2067, 2016.
  • Internet: Emotive Headsets, https://www.emotiv.com/product-category/mobile-eeg headsets/, 14.11.2018.
  • Internet: NeuroSky Brainwave, https://store.neurosky.com/, 14.11.2018.
  • S. Koelstra, C. Mühl, and M. Soleymani, ‘’DEAP: A Database for Emotion Analysis: Using Physiological Signals’’, IEEE Transactions on Affective Computing, 3(1), 18-31, 2012.
  • R. Cooper, J. W. Osselton, and J. C. Shaw, ‘’EEG Technology’’, Butterworth & CO., 1969.
  • G. H. Klem, H. O. Luders, H. H. Jasper., and C. Elger., ‘’The Ten-Twenty Electrode System of the International Federation’’, Electroencephalography and Clinical Neurophysiology Supplement, 3-6, 1999.
  • P. Ekman, ‘’An Argument for Basic Emotions’’, Cognition and Emotion, 6(3-4), 169-200, 1992.
  • J. A. Russel., ‘’Core Affect and Psychological Construction of Emotion’’, Psychological Review, 110(1), 145-150, 2003.
  • Q. Zhang,, X. Chen, Q. Zhan, T. Yang, and S. Xia, ‘’Respiration-Based Emotion Recognition with Deep Learning’’, Computers in Industry, 92(2017) 84-90, 2017.
  • A. Mert, and A. Akan, ‘’Emotion Recognition Based on Time-Frequency Distribution of EEG Signals Using Multivariate Synchrosqueezing Transform’’, Digital Signal Processing, 81(2018), 152-157, 2018.
  • Y. Zhang, X. Ji, and S. Zhang, ‘’An Approach to EEG-Based Recognition Using Combined Feature Extraction Method’’, Neuroscience Letters , 633, 152-157, 2016.
  • L. Xin, S. Xiao-Qi, Q. Xiao-Ying, and S. Xiao-Feng, ‘’Relevance Vector Machine Based EEG Emotion Recognition’’, 2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control, China, 293-297, 2016.
  • T. B. Alakuş and I. Turkoglu, EEG Verilerilnden İşaret İşleme ve Sınıflandırma Teknikleri Kullanılarak Duygu Tahmini, Yüksek Lisans Tezi, Fırat Üniversitesi, Fen Bilimleri Enstitüsü, 2018.
  • B. Krisnandhika, A. Faqih, P. D. Pumamasari, and B. Kusumoputro, ‘’ Emotion Recognition System Based on EEG Signals Using Relative Wavelet Energy Features and a Modified Radial Basis Function Neural Networks’’, 2017 International Confference on Consumer Electronics and Devices, London, 50-54, 2017.
  • S. Aydın, H. M. Saraoğlu, S. Kara, ‘’Log Energy Entropy-Based EEG Classification with Multilayer Neural Networks in Seizure’’, Annals of Biomedical Engineering,’’, 37(12), 2626-2630, 2009.
  • A. Accardo, M. Affinito, M. Carrozzi, and F. Bouqet, ‘’Use of the Fractal Dimension for the Analysis of Electroencephalographic Time Series’’, Biological Cybernetics, 77(5), 339-350, 1997.
  • A. Al-Nuaimi, E. Jammeh, L. Sun, and E. Ifeachor, ‘’ Higuchi Fractal Dimension of the Electroensephalogram as a Biomarker for Early Detection of Alzheimers Disease, IEEE Engineering in Medicine and Biology Society Annual Conference, 2320-2324, 2017.
  • Calp, M. H., “Medical Diagnosis with a Novel SVM-CoDOA Based Hybrid Approach”, BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(4), 6-16, (2018).

Determination of Effective EEG Channels for Discrimination of Positive and Negative Emotions with Wavelet Decomposition and Support Vector Machines

Year 2019, Volume: 12 Issue: 3, 229 - 237, 31.07.2019
https://doi.org/10.17671/gazibtd.482939

Abstract

People’s
lives and decision-making process are influenced by negative-positive emotions.
People state their emotions with words, body language, facial expression and
voice during thinking, decision making, observing or interacting with the
environment. So, it is vital to understand the nature of emotions well. EEG
based emotion recognition systems are useful in brain-computer interface (BCI)
area. BCI systems are applied in various fields such as education, healthcare
systems, virtual reality, video gaming industry. Although EEG signals give much
valuable information about brain functions and emotions, brain-computer
interface systems have not attained the targeted goals because of artefacts,
misuse of EEG channels, data complexity and inappropriate feature extraction
and selection methods. In this article, we tried to analyze which EEG channels
are effective to estimate positive-negative emotions. We applied publicly
available dataset (DEAP) in this work and 32 different EEG channels were
classified. Discrete wavelet decomposition, information measurement and
statistical methods were applied in the feature extraction phase. In the last
phase, SVM (Support Vector Machines) are applied in order to classify the
features. The classification performance of the proposed method evaluated by
classification accuracy, log-loss, and ten-fold cross validation. Performance
accuracy was observed from each EEG channel and average accuracy was found 74%.
The experimental results indicated that the best EEG channels for
positive-negative emotions Fp1, FC6, C4, CP1, CP5, CP6, T7, P7, and Pz via the
proposed method.

References

  • M. Naji, M. Firoozabadi, and P. Azadfallah, ‘’Emotion Classification During Music Listening from Forehead Biosignals’’, Signal, Image and Video Processing, 9(6), 1365-1375, 2015.
  • A. Turnip, A. I. Simbolon, M. F. Amri, P. Sihombing, R. H. Setiadi., and E. Mulyana, ‘’Backpropagation Neural Networks Training for EEG-SSVEP Classification of Emotion Recognition’’, Internetworking Indenosia Journal, 9(1), 53-57, 2017
  • S. J. Westerman, P. H. Gardner, and E. J. Sutherland, ‘’Usability Testing Emotion-Orianted Computing Systems: Psychometric Assessment’’, HUMAINE Deliverable, 1-53, 2006.
  • M. Lhommet, and S. C. Marsella, ‘’Expressing Emotion through Posture and Gesture’’, The Oxford Handbook of Affective Computing, Oxford Library of Psychology, Oxford, 2015.
  • T. B. Alakuş, and İ. Türoğlu, ‘’EEG Based Emotion Analysis Systems’’, Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 11(1), 26-39, 2018.
  • W. Szwoch, ‘’Using Physiological Signals for Emotion’’, Sopot, Poland, 2013.
  • J. Pan., Y. Li., and J. Wang, ‘’An EEG-Based Brain-Computer Interface for Emotion Recognition’’, 2016 International Joint Conference on Neural Networks, Canada, 2063-2067, 2016.
  • Internet: Emotive Headsets, https://www.emotiv.com/product-category/mobile-eeg headsets/, 14.11.2018.
  • Internet: NeuroSky Brainwave, https://store.neurosky.com/, 14.11.2018.
  • S. Koelstra, C. Mühl, and M. Soleymani, ‘’DEAP: A Database for Emotion Analysis: Using Physiological Signals’’, IEEE Transactions on Affective Computing, 3(1), 18-31, 2012.
  • R. Cooper, J. W. Osselton, and J. C. Shaw, ‘’EEG Technology’’, Butterworth & CO., 1969.
  • G. H. Klem, H. O. Luders, H. H. Jasper., and C. Elger., ‘’The Ten-Twenty Electrode System of the International Federation’’, Electroencephalography and Clinical Neurophysiology Supplement, 3-6, 1999.
  • P. Ekman, ‘’An Argument for Basic Emotions’’, Cognition and Emotion, 6(3-4), 169-200, 1992.
  • J. A. Russel., ‘’Core Affect and Psychological Construction of Emotion’’, Psychological Review, 110(1), 145-150, 2003.
  • Q. Zhang,, X. Chen, Q. Zhan, T. Yang, and S. Xia, ‘’Respiration-Based Emotion Recognition with Deep Learning’’, Computers in Industry, 92(2017) 84-90, 2017.
  • A. Mert, and A. Akan, ‘’Emotion Recognition Based on Time-Frequency Distribution of EEG Signals Using Multivariate Synchrosqueezing Transform’’, Digital Signal Processing, 81(2018), 152-157, 2018.
  • Y. Zhang, X. Ji, and S. Zhang, ‘’An Approach to EEG-Based Recognition Using Combined Feature Extraction Method’’, Neuroscience Letters , 633, 152-157, 2016.
  • L. Xin, S. Xiao-Qi, Q. Xiao-Ying, and S. Xiao-Feng, ‘’Relevance Vector Machine Based EEG Emotion Recognition’’, 2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control, China, 293-297, 2016.
  • T. B. Alakuş and I. Turkoglu, EEG Verilerilnden İşaret İşleme ve Sınıflandırma Teknikleri Kullanılarak Duygu Tahmini, Yüksek Lisans Tezi, Fırat Üniversitesi, Fen Bilimleri Enstitüsü, 2018.
  • B. Krisnandhika, A. Faqih, P. D. Pumamasari, and B. Kusumoputro, ‘’ Emotion Recognition System Based on EEG Signals Using Relative Wavelet Energy Features and a Modified Radial Basis Function Neural Networks’’, 2017 International Confference on Consumer Electronics and Devices, London, 50-54, 2017.
  • S. Aydın, H. M. Saraoğlu, S. Kara, ‘’Log Energy Entropy-Based EEG Classification with Multilayer Neural Networks in Seizure’’, Annals of Biomedical Engineering,’’, 37(12), 2626-2630, 2009.
  • A. Accardo, M. Affinito, M. Carrozzi, and F. Bouqet, ‘’Use of the Fractal Dimension for the Analysis of Electroencephalographic Time Series’’, Biological Cybernetics, 77(5), 339-350, 1997.
  • A. Al-Nuaimi, E. Jammeh, L. Sun, and E. Ifeachor, ‘’ Higuchi Fractal Dimension of the Electroensephalogram as a Biomarker for Early Detection of Alzheimers Disease, IEEE Engineering in Medicine and Biology Society Annual Conference, 2320-2324, 2017.
  • Calp, M. H., “Medical Diagnosis with a Novel SVM-CoDOA Based Hybrid Approach”, BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(4), 6-16, (2018).
There are 24 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Articles
Authors

Talha Burak Alakuş 0000-0003-3136-3341

İbrahim Türkoğlu 0000-0003-4938-4167

Publication Date July 31, 2019
Submission Date November 15, 2018
Published in Issue Year 2019 Volume: 12 Issue: 3

Cite

APA Alakuş, T. B., & Türkoğlu, İ. (2019). Pozitif ve Negatif Duyguların Ayrımında Etkili EEG Kanallarının Dalgacık Dönüşümü ve Destek Vektör Makineleri ile Belirlenmesi. Bilişim Teknolojileri Dergisi, 12(3), 229-237. https://doi.org/10.17671/gazibtd.482939