Emotion Recognition Based on Framework of BADEBA-SVM

Brain-computer interface (BCI) provides a new communication channel between human brain and computer. In order to eliminate uncorrelated channels to improve BCI performance and enhance user convenience with fewer channels, this paper proposes a new framework using binary adaptive differential evolut...

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Bibliographic Details
Published in:Mathematical problems in engineering Vol. 2019; no. 2019; pp. 1 - 9
Main Authors: Wang, Zhongmin, Wang, Wenlang, Zhang, Zhaoping
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 2019
Hindawi
John Wiley & Sons, Inc
Subjects:
Accuracy
Adaptive algorithms
Arousal
Channels
Classification
Electroencephalography
Emotion recognition
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Human-computer interface
Intelligence
Mathematical problems
Medical research
Mutation
Neurophysiology
Neurosciences
Particle swarm optimization
Physiology
Population
Signal processing
Spatial distribution
Support vector machines
Wavelet transforms
ISSN:1024-123X, 1563-5147
Online Access:Get full text
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Summary:Brain-computer interface (BCI) provides a new communication channel between human brain and computer. In order to eliminate uncorrelated channels to improve BCI performance and enhance user convenience with fewer channels, this paper proposes a new framework using binary adaptive differential evolution bat algorithm (BADEBA). The framework uses the important ideas of differential evolution algorithm and bat algorithm to select electroencephalograph (EEG) channels and intelligently optimizes the parameters of support vector machine (SVM). It combines wavelet packet transform (WPT) and common space pattern (CSP) to achieve the goal of using fewer channels to obtain the best classification accuracy. The proposed framework is evaluated with a common data set (DEAP). The results show that, compared with genetic algorithm (GA), binary particle swarm optimization (BPSO) and bat algorithm, the proposed BADEBA in this framework only uses eight channels to improve the classification accuracy by 13.63% in the valence dimension and seven channels to improve the classification accuracy by 15.22% in the arousal dimension. In addition, the spatial distribution of the best channels selected by this method is consistent with the existing knowledge of brain structure and neurophysiology, which shows the accuracy and validity of this method.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2019/9875250