Basic and Clinical Neuroscience Journal
مجله علوم اعصاب پایه و بالینی
BCN
Medical Sciences
http://bcn.iums.ac.ir
137
journal137
2008-126X
2228-7442
10.32598/bcn
en
jalali
1396
12
1
gregorian
2018
3
1
0
Accepted Articles
online
1
fulltext
en
Feature Extraction with Stacked Autoencoders for EEG Channel Reduction in Emotion Recognition
Cognitive Neuroscience
Cognitive Neuroscience
Original
Original
<div style="text-align: justify;"><span style="font-size:14px;"><span style="font-family:Tahoma;"><span style="line-height:2;">Emotion recognition by EEG signals is one of the complex methods because the extraction and recognition of the features that are hidden in the signal are sophisticated and require a significant number of EEG channels. Presenting a method for feature analysis and an algorithm for reducing the number of EEG channels fulfills the needs for research in this field. Therefore, this study has investigated the possibility of utilizing deep learning to reduce the number of channels while maintaining the quality of the EEG signal. Stacked autoencoder network (SAEs) is used to extract optimal features for the classification of emotion in valence and arousal dimensions. Autoencoder networks have the ability to extract complex features to provide linear and non-linear features which are a good representative of the signal. The accuracy of conventional emotion recognition classifier (SVM) using feature extracted from SAEs was obtained 75.7% for valence and 74.4% for arousal dimensions, respectively. Further analysis also illustrates that valence dimension detection with reduced EEG channels has a different composition of EEG channels compared to arousal dimension. In addition, the number of channels is reduced from 32 to 12, which is an excellent development to design a small size EEG device by applying these optimal features.</span></span></span></div>
Deep learning, Stacked auto-encoder (SAE), Channel reduction, EEG analysis, Emotion
0
0
http://bcn.iums.ac.ir/browse.php?a_code=A-10-5138-2&slc_lang=en&sid=1
Elnaz
Vafaei
elnaz.vafaei@srbiau.ac.ir
13700319475328460043906
13700319475328460043906
No
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Fereidoun
Nowshiravan Rahatabad
nooshiravan@gmail.com
13700319475328460043907
13700319475328460043907
Yes
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Seyed Kamaledin
Setarehdan
ksetareh@ut.ac.ir
13700319475328460043908
13700319475328460043908
No
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Parviz
Azadfallah
azadfa_p@modares.ac.ir
13700319475328460043909
13700319475328460043909
No
Tarbiat Modares University, Tehran, Iran.