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Cultural Differences and Similarities in Emotion Recognition

Published: 02 September 2015 Publication History

Abstract

The electroencephalogram (EEG) is a powerful method for investigation of different cognitive processes. Recently, EEG analysis became very popular and important, where classification of these signals stands out as one of the mostly used methodologies. Emotion recognition is one of the most challenging tasks in EEG analysis since not much is known about representation of different emotion in EEG signals. In addition, inducing of desired emotion is by itself difficult, since various individuals react differently to external stimuli (audio, video, etc.) In this paper, we will examine the similarities in emotion perception of different individuals on the basis of audio stimuli. Since some of the participants in the experiment did not understand the language of the stimuli, we will also investigate the impact of language understanding on emotion perception. This study presents some preliminary results of more complex experiments in the area of affective computing that are planned.

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Cited By

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  • (2024)Exploring the Utility of Emotion Recognition Systems in HealthcareUsing Machine Learning to Detect Emotions and Predict Human Psychology10.4018/979-8-3693-1910-9.ch011(245-271)Online publication date: 12-Apr-2024
  • (2022)Elastic distances for time-series classification: Itakura versus Sakoe-Chiba constraintsKnowledge and Information Systems10.1007/s10115-022-01725-164:10(2797-2832)Online publication date: 1-Oct-2022
  • (2018)Emotion perception and recognition: An exploration of cultural differences and similaritiesCognitive Systems Research10.1016/j.cogsys.2018.06.00952(103-116)Online publication date: Dec-2018

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cover image ACM Other conferences
BCI '15: Proceedings of the 7th Balkan Conference on Informatics Conference
September 2015
293 pages
ISBN:9781450333351
DOI:10.1145/2801081
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • UCV: University of Craiova

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Association for Computing Machinery

New York, NY, United States

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Published: 02 September 2015

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Author Tags

  1. Artificial Intelligence
  2. Classification
  3. Emotion detection
  4. Time-series mining

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BCI '15
BCI '15: 7th Balkan Conference in Informatics
September 2 - 4, 2015
Craiova, Romania

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BCI '15 Paper Acceptance Rate 32 of 74 submissions, 43%;
Overall Acceptance Rate 97 of 250 submissions, 39%

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Cited By

View all
  • (2024)Exploring the Utility of Emotion Recognition Systems in HealthcareUsing Machine Learning to Detect Emotions and Predict Human Psychology10.4018/979-8-3693-1910-9.ch011(245-271)Online publication date: 12-Apr-2024
  • (2022)Elastic distances for time-series classification: Itakura versus Sakoe-Chiba constraintsKnowledge and Information Systems10.1007/s10115-022-01725-164:10(2797-2832)Online publication date: 1-Oct-2022
  • (2018)Emotion perception and recognition: An exploration of cultural differences and similaritiesCognitive Systems Research10.1016/j.cogsys.2018.06.00952(103-116)Online publication date: Dec-2018

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