Emotional state classification from EEG data using machine learning approach

Recently, emotion classification from EEG data has attracted much attention with the rapid development of dry electrode techniques, machine learning algorithms, and various real-world applications of brain–computer interface for normal people. Until now, however, researchers had little understanding...

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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 129; S. 94 - 106
Hauptverfasser: Wang, Xiao-Wei, Nie, Dan, Lu, Bao-Liang
Format: Journal Article Tagungsbericht
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 10.04.2014
Elsevier
Schlagworte:
Applied sciences
Artificial intelligence
Biological and medical sciences
Brain–computer interface
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Data processing. List processing. Character string processing
Electrodiagnosis. Electric activity recording
Electroencephalograph
Emotion classification
Exact sciences and technology
Feature reduction
Investigative techniques, diagnostic techniques (general aspects)
Manifold learning
Medical sciences
Memory organisation. Data processing
Nervous system
Software
Support vector machine
Electroencephalograph
Support vector machine
Manifold learning
Emotion classification
Feature reduction
Brain–computer interface
Brain
Smoothing methods
Electroencephalography
Emotion emotionality
Dynamical system
Dynamic method
Data reduction
Electrodes
Brain-computer interface
Dimension reduction
Experimental result
Multidimensional analysis
User interface
Vector support machine
Selection criterion
Reduced order model
Artificial intelligence
Power spectrum
ISSN:0925-2312, 1872-8286
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Zusammenfassung:Recently, emotion classification from EEG data has attracted much attention with the rapid development of dry electrode techniques, machine learning algorithms, and various real-world applications of brain–computer interface for normal people. Until now, however, researchers had little understanding of the details of relationship between different emotional states and various EEG features. To improve the accuracy of EEG-based emotion classification and visualize the changes of emotional states with time, this paper systematically compares three kinds of existing EEG features for emotion classification, introduces an efficient feature smoothing method for removing the noise unrelated to emotion task, and proposes a simple approach to tracking the trajectory of emotion changes with manifold learning. To examine the effectiveness of these methods introduced in this paper, we design a movie induction experiment that spontaneously leads subjects to real emotional states and collect an EEG data set of six subjects. From experimental results on our EEG data set, we found that (a) power spectrum feature is superior to other two kinds of features; (b) a linear dynamic system based feature smoothing method can significantly improve emotion classification accuracy; and (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2013.06.046