A Spectro-Statistical Approach for Emotion Identification from EEG Signals

Lownish Rai Sookha, Gulshan Sharma, M. A. Ganaie, Abhinav Dhall

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publication2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9798350394948
DOIs
Publication statusPublished - 11 Jul 2024
Event18th IEEE International Conference on Automatic Face and Gesture Recognition - Istanbul, Turkey
Duration: 27 May 202431 May 2024
Conference number: 18th
https://fg2024.ieee-biometrics.org/conference-program/

Publication series

NameIEEE International Conference on Automatic Face and Gesture Recognition
PublisherInstitute of Electrical and Electronics Engineers
Volume2024
ISSN (Electronic)2770-8330

Conference

Conference18th IEEE International Conference on Automatic Face and Gesture Recognition
Abbreviated titleFG 2024
Country/TerritoryTurkey
CityIstanbul
Period27/05/2431/05/24
Internet address

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