Two-stepped majority voting for efficient EEG-based emotion classification

Two-stepped majority voting for efficient EEG-based emotion classification

Abstract In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human–machine interactions. Facial features- and body gestures-based approaches have been generally proposed for emot...

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Bibliographic Details
Main Authors: Aras M. Ismael, Ömer F. Alçin, Karmand Hussein Abdalla, Abdulkadir Şengür
Format: Article
Language:English
Published: SpringerOpen 2020-09-01
Series:Brain Informatics
Subjects:
EEG-based emotion recognition
EEG rhythms
Wavelet packet entropies
Fractal dimensions
Majority voting
Online Access:http://link.springer.com/article/10.1186/s40708-020-00111-3

Internet

http://link.springer.com/article/10.1186/s40708-020-00111-3

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