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Fusion of Time and Frequency Domain Attributes for the Automated Recognition of Emotion using EEG Recordings

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Emotions have a significant impact on a person's behavior. Expression reflects how people perceive events, interactions, and judgment. It is possible to identify emotions and categorize them using several techniques, including electroencephalography (EEG) signals, gestures, facial expressions, speech patterns, etc. However, emotion recognition using physiological signals has gained popularity owing to its authenticity. In this study, 54 attributes were extracted from each EEG channel to gather emotional information. These attributes were then fed to multiple classifiers and the results were compared. The study attained a maximum accuracy of 89.52% for channel T7 - 24 using ensemble bagged tree classifiers.

Original languageEnglish
Title of host publicationOCIT 2023 - 21st International Conference on Information Technology, Proceedings
EditorsLaavanya Rachakonda, Niranjan K. Ray, Dilip Singh Sisodia, Jitendra Kumar Rout
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages249-253
Number of pages5
ISBN (Electronic)9798350358230
DOIs
Publication statusPublished - 2023
Event21st OITS International Conference on Information Technology, OCIT 2023 - Raipur, India
Duration: 13-12-202315-12-2023

Publication series

NameOCIT 2023 - 21st International Conference on Information Technology, Proceedings

Conference

Conference21st OITS International Conference on Information Technology, OCIT 2023
Country/TerritoryIndia
CityRaipur
Period13-12-2315-12-23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Statistics, Probability and Uncertainty
  • Modelling and Simulation

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