An EEG Database and Its Initial Benchmark Emotion Classification Performance
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50017056" target="_blank" >RIV/62690094:18450/20:50017056 - isvavai.cz</a>
Result on the web
<a href="https://www.hindawi.com/journals/cmmm/2020/8303465/" target="_blank" >https://www.hindawi.com/journals/cmmm/2020/8303465/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1155/2020/8303465" target="_blank" >10.1155/2020/8303465</a>
Alternative languages
Result language
angličtina
Original language name
An EEG Database and Its Initial Benchmark Emotion Classification Performance
Original language description
Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications. However, most of the state-of-the-art methods identified emotions after analyzing facial images. Emotion recognition using electroencephalogram (EEG) signals has got less attention. However, the advantage of using EEG signals is that it can capture real emotion. However, very few EEG signals databases are publicly available for affective computing. In this work, we present a database consisting of EEG signals of 44 volunteers. Twenty-three out of forty-four are females. A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos. So, 3 video files are devoted to each emotion. Participants are mapped with the emotion that they had felt after watching each video. The recorded EEG signals are considered further to classify four types of emotions based on discrete wavelet transform and extreme learning machine (ELM) for reporting the initial benchmark classification performance. The ELM algorithm is used for channel selection followed by subband selection. The proposed method performs the best when features are captured from the gamma subband of the FP1-F7 channel with 94.72% accuracy. The presented database would be available to the researchers for affective recognition applications.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10602 - Biology (theoretical, mathematical, thermal, cryobiology, biological rhythm), Evolutionary biology
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
ISSN
1748-670X
e-ISSN
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Volume of the periodical
2020
Issue of the periodical within the volume
August
Country of publishing house
GB - UNITED KINGDOM
Number of pages
14
Pages from-to
"Article Number: 8303465"
UT code for WoS article
000562862400002
EID of the result in the Scopus database
2-s2.0-85090070892