Emotion Recognition from Single-Trial EEG Based on Kernel Fisher’s Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine
Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data...
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doaj-5e2355dbd6904ae1827bb55a9e00d43f2020-11-24T21:06:33ZengMDPI AGSensors1424-82202014-07-01148133611338810.3390/s140813361s140813361Emotion Recognition from Single-Trial EEG Based on Kernel Fisher’s Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector MachineYi-Hung Liu0Chien-Te Wu1Wei-Teng Cheng2Yu-Tsung Hsiao3Po-Ming Chen4Jyh-Tong Teng5Department of Mechanical Engineering, Chung Yuan Christian University, Chungli 32023, TaiwanSchool of Occupational Therapy, College of Medicine, National Taiwan University, Taipei 10051, TaiwanDepartment of Mechanical Engineering, Chung Yuan Christian University, Chungli 32023, TaiwanDepartment of Mechanical Engineering, Chung Yuan Christian University, Chungli 32023, TaiwanDepartment of Mechanical Engineering, Chung Yuan Christian University, Chungli 32023, TaiwanDepartment of Mechanical Engineering, Chung Yuan Christian University, Chungli 32023, TaiwanElectroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher’s discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher’s emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.http://www.mdpi.com/1424-8220/14/8/13361EEGemotion recognitionhealth carebrain-computer interfacesupport vector machine |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yi-Hung Liu Chien-Te Wu Wei-Teng Cheng Yu-Tsung Hsiao Po-Ming Chen Jyh-Tong Teng |
spellingShingle |
Yi-Hung Liu Chien-Te Wu Wei-Teng Cheng Yu-Tsung Hsiao Po-Ming Chen Jyh-Tong Teng Emotion Recognition from Single-Trial EEG Based on Kernel Fisher’s Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine Sensors EEG emotion recognition health care brain-computer interface support vector machine |
author_facet |
Yi-Hung Liu Chien-Te Wu Wei-Teng Cheng Yu-Tsung Hsiao Po-Ming Chen Jyh-Tong Teng |
author_sort |
Yi-Hung Liu |
title |
Emotion Recognition from Single-Trial EEG Based on Kernel Fisher’s Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine |
title_short |
Emotion Recognition from Single-Trial EEG Based on Kernel Fisher’s Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine |
title_full |
Emotion Recognition from Single-Trial EEG Based on Kernel Fisher’s Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine |
title_fullStr |
Emotion Recognition from Single-Trial EEG Based on Kernel Fisher’s Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine |
title_full_unstemmed |
Emotion Recognition from Single-Trial EEG Based on Kernel Fisher’s Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine |
title_sort |
emotion recognition from single-trial eeg based on kernel fisher’s emotion pattern and imbalanced quasiconformal kernel support vector machine |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2014-07-01 |
description |
Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher’s discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher’s emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods. |
topic |
EEG emotion recognition health care brain-computer interface support vector machine |
url |
http://www.mdpi.com/1424-8220/14/8/13361 |
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