Abstract
Automatic identification of emotions is important in human-centered computing. It allows machines to better understand user emotions. Identifying emotions via neural sensing techniques such as electroencephalogram (EEG) is a promising approach. In this paper, we aim to identify the emotions class from EEG signals. We frame emotion identification as a classification task and apply spectral and statistical encoders to extract the relevant features. We validate our approach on EmoNeuroDB dataset. Our method outperforms the EmoNeuroDB baseline, achieving a 42.10% increase in class prediction accuracy.
Original language | English |
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Title of host publication | 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG) |
Publisher | Institute of Electrical and Electronics Engineers |
Number of pages | 5 |
ISBN (Electronic) | 9798350394948 |
DOIs | |
Publication status | Published - 11 Jul 2024 |
Event | 18th IEEE International Conference on Automatic Face and Gesture Recognition - Istanbul, Turkey Duration: 27 May 2024 → 31 May 2024 Conference number: 18th https://fg2024.ieee-biometrics.org/conference-program/ |
Publication series
Name | IEEE International Conference on Automatic Face and Gesture Recognition |
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Publisher | Institute of Electrical and Electronics Engineers |
Volume | 2024 |
ISSN (Electronic) | 2770-8330 |
Conference
Conference | 18th IEEE International Conference on Automatic Face and Gesture Recognition |
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Abbreviated title | FG 2024 |
Country/Territory | Turkey |
City | Istanbul |
Period | 27/05/24 → 31/05/24 |
Internet address |