article

Emotional state classification from EEG data using machine learning approach

Authors:

Bao-Liang LuAuthors Info & Claims

Neurocomputing, Volume 129

Pages 94 - 106

https://doi.org/10.1016/j.neucom.2013.06.046

Published: 01 April 2014 Publication History

Abstract

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.

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Wang YZhang BDi L(2024)Research Progress of EEG-Based Emotion Recognition: A SurveyACM Computing Surveys10.1145/366600256:11(1-49)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3666002
Huang WChen YJiang XGao CChen QZhang TYan BWang YYang JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Correlation-Driven Multi-Modality Graph Decomposition for Cross-Subject Emotion RecognitionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681579(2272-2281)Online publication date: 28-Oct-2024
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cover image Neurocomputing

Neurocomputing Volume 129, Issue

April, 2014

596 pages

ISSN:0925-2312

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Copyright © Elsevier B.V. © 2013.

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Elsevier Science Publishers B. V.

Netherlands

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Published: 01 April 2014

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Cited By

Wang YZhang BDi L(2024)Research Progress of EEG-Based Emotion Recognition: A SurveyACM Computing Surveys10.1145/366600256:11(1-49)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3666002
Huang WChen YJiang XGao CChen QZhang TYan BWang YYang JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Correlation-Driven Multi-Modality Graph Decomposition for Cross-Subject Emotion RecognitionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681579(2272-2281)Online publication date: 28-Oct-2024
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Cang XGuerra RGuta BBucci PRodgers LMah HFeng QAgrawal AMacLean K(2024)FEELing (key)Pressed: Implicit Touch Pressure Bests Brain Activity for Modeling Emotion Dynamics in the Space Between Stressed & RelaxedIEEE Transactions on Haptics10.1109/TOH.2023.330805917:3(310-318)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TOH.2023.3308059
Hu LTan CXu JQiao RHu YTian Y(2024)Decoding emotion with phase–amplitude fusion features of EEG functional connectivity networkNeural Networks10.1016/j.neunet.2024.106148172:COnline publication date: 25-Jun-2024
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Chaurasia AFallahi MStrufe TTerhörst PCabarcos P(2024)NeuroIDBenchJournal of Information Security and Applications10.1016/j.jisa.2024.10383285:COnline publication date: 1-Sep-2024
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Yang LWang YOuyang RNiu XYang XZheng C(2024)Electroencephalogram-based emotion recognition using factorization temporal separable convolution networkEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108011133:PAOnline publication date: 1-Jul-2024
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Hosseini IHossain MZhang YRahman S(2024)Deep learning model for simultaneous recognition of quantitative and qualitative emotion using visual and bio-sensing dataComputer Vision and Image Understanding10.1016/j.cviu.2024.104121248:COnline publication date: 1-Nov-2024
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Shi XShe QFang FMeng MTan TZhang Y(2024)Enhancing cross-subject EEG emotion recognition through multi-source manifold metric transfer learningComputers in Biology and Medicine10.1016/j.compbiomed.2024.108445174:COnline publication date: 17-Jul-2024
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