Electrodermal Activity Based Emotion Recognition using Time-Frequency Methods and Machine Learning Algorithms

Abstract: In this work, the feasibility of time-frequency methods, namely short-time Fourier transform, Choi Williams distribution, and smoothed pseudo-Wigner-Ville distribution in the classification of happy and sad emotional states using Electrodermal activity signals have been explored. For this, the annotated happy and sad signals are obtained from an online public database and decomposed into phasic components. The time-frequency analysis has been performed on the phasic components using three different methods. Four statistical features, namely mean, variance, kurtosis, and skewness are extracted from each method. Four classifiers, namely logistic regression, Naive Bayes, random forest, and support vector machine, have been used for the classification. The combination of the smoothed pseudo-Wigner-Ville distribution and random forest yields the highest F-measure of 68.74% for classifying happy and sad emotional states. Thus, it appears that the suggested technique could be helpful in the diagnosis of clinical conditions linked to happy and sad emotional states.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Electrodermal Activity Based Emotion Recognition using Time-Frequency Methods and Machine Learning Algorithms ; volume:7 ; number:2 ; year:2021 ; pages:863-866 ; extent:4 ; Current directions in biomedical engineering ; 7, Heft 2 (2021), 863-866 (gesamt 4)

Contributors, Places and Time

• Creator: Rao Veeranki, Yedukondala ; Ganapathy, Nagarajan ; Swaminathan, Ramakrishnan

Further information

• DOI: 10.1515/cdbme-2021-2220
• URN: urn:nbn:de:101:1-2022101214261530258190
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:30 AM CEST