The challenges of emotion recognition methods based on electroencephalogram signals: a literature review
Electroencephalogram (EEG) signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by: i) the distribution of the data used, ii) consider of differences in participant characteristics, and iii) consider the characteristics of the...
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Main Authors: | , , |
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Format: | Other NonPeerReviewed |
Language: | English |
Published: |
International Journal of Electrical and Computer Engineering
2022
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Subjects: | |
Online Access: | https://repository.ugm.ac.id/284299/1/The-challenges-of-emotion-recognition-methods-based-on-electroencephalogram-signals-A-literature-reviewInternational-Journal-of-Electrical-and-Computer-Engineering.pdf https://repository.ugm.ac.id/284299/ https://ijece.iaescore.com/index.php/IJECE/article/view/25953 |
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Institution: | Universitas Gadjah Mada |
Language: | English |
Summary: | Electroencephalogram (EEG) signals in recognizing emotions have several
advantages. Still, the success of this study, however, is strongly influenced
by: i) the distribution of the data used, ii) consider of differences in
participant characteristics, and iii) consider the characteristics of the EEG
signals. In response to these issues, this study will examine three important
points that affect the success of emotion recognition packaged in several
research questions: i) What factors need to be considered to generate and
distribute EEG data?, ii) How can EEG signals be generated with
consideration of differences in participant characteristics?, and iii) How do
EEG signals with characteristics exist among its features for emotion
recognition? The results, therefore, indicate some important challenges to be
studied further in EEG signals-based emotion recognition research. These
include i) determine robust methods for imbalanced EEG signals data, ii)
determine the appropriate smoothing method to eliminate disturbances on
the baseline signals, iii) determine the best baseline reduction methods to
reduce the differences in the characteristics of the participants on the EEG
signals, iv) determine the robust architecture of the capsule network method
to overcome the loss of knowledge information and apply it in more diverse
data set. |
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