EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown fea...

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Vydané v:TheScientificWorld Ročník 2014; číslo 2014; s. 1 - 10
Hlavní autori: Jirayucharoensak, Suwicha, Israsena, P., Pan-ngum, Setha
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cairo, Egypt Hindawi Publishing Corporation 01.01.2014
John Wiley & Sons, Inc
Wiley
Predmet:
Accuracy
Algorithms
Architectural engineering
Arousal - physiology
Brain research
Classification
Electrodes
Electroencephalography
Electroencephalography - methods
Emotions
Emotions - physiology
Humans
Identification and classification
Methods
Nerve Net
Neural Networks, Computer
Principal Component Analysis - methods
Principal components analysis
Reproducibility of Results
Signal processing
Support Vector Machine
Task Performance and Analysis
ISSN:2356-6140, 1537-744X, 1537-744X
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Shrnutí:Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Academic Editor: Jinshan Tang
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2014/627892