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Cited by 213Emotion and Mood RecognitionEEG and Brain-Computer InterfacesFace and Expression Recognition

The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.

Optimal Feature Selection and Deep Learning Ensembles Method for Emotion Recognition From Human Brain EEG Sensors

Open access
Majid Mehmood, Raja, Du, Ruoyu, Lee, Hyo Jong·2017·IEEE Access
DOI 10.1109/access.2017.2724555
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Abstract

Recent advancements in human-computer interaction research have led to the possibility of emotional communication via brain-computer interface systems for patients with neuropsychiatric disorders or disabilities. In this paper, we efficiently recognize emotional states by analyzing the features of electroencephalography (EEG) signals, which are generated from EEG sensors that noninvasively measure the electrical activity of neurons inside the human brain, and select the optimal combination of these features for recognition. In this paper, the scalp EEG data of 21 healthy subjects (12-14 years old) were recorded using a 14-channel EEG machine while the subjects watched images with four types of emotional stimuli (happy, calm, sad, or scared). After preprocessing, the Hjorth parameters (activity, mobility, and complexity) were used to measure the signal activity of the time series data. We selected the optimal EEG features using a balanced one-way ANOVA after calculating the Hjorth parameters for different frequency ranges. Features selected by this statistical method outperformed univariate and multivariate features. The optimal features were further processed for emotion classification using support vector machine, k-nearest neighbor, linear discriminant analysis, Naive Bayes, random forest, deep learning, and four ensembles methods (bagging, boosting, stacking, and voting). The results show that the proposed method substantially improves the emotion recognition rate with respect to the commonly used spectral power band method.

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  1. 2014, Emotiv EPOC Brain computer interface & scientific contextual EEG

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Metadata

Authors
Majid Mehmood, R., Du, R., & Lee, H. J.
Affiliations
  • Nanjing University of Posts and Telecommunications (CN)
  • Jeonbuk National University (KR)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2017
Volume
5
Pages
14797-14806
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2017.2724555
OpenAlex
W2740113900
Cited by
213
References
43
Citations by year
2026: 6 · 2025: 17 · 2024: 20 · 2023: 31 · 2022: 27 · 2021: 40 · 2020: 29 · 2019: 29 · 2018: 14
FWCI
25.65
Type
journal article
Language
en
Open access
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
Locations
  • IEEE Access (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Brain Korea 21 PLUS Project
  • National Research Foundation (NRF) of Korea
  • Ministry of Science, ICT and Future Planning, Korea, under the Information Technology Research Center, supervised by the Institute for Information and Communications Technology Promotion (IITP-2016-R0992-16-1023)
  • Basic Science Research Program through the NRF of South Korea, through the Ministry of Education (GR 2016R1D1A3B03931911)
  • National Natural Science Foundation for Young Scholars of China (61603198)
  • Natural Science Foundation for Young Scholars of Jiangsu Province (BK20160918)
  • NUPTSF (NY214194)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
Keywords
emotion recognition, EEG, feature selection, Hjorth parameters, brain-computer interface, EEG classification, ensemble learning, emotional stimuli, children

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