Transformation of EEG signal for emotion analysis and dataset construction for DNN learning

Yeahoon Kwon, Yiyan Nan, Shin-Dug Kim

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

Abstract

This work is to design an emotional analysis system using Deep Neural Network based on electroencephalogram data. The data are processed using high pass filtering and removing DC offset method in the proposed system. Then the preprocessed dataset is constructed to analysis the impact of input data placement on recognition performance. In the experiment, the happy and neutral dataset are used to measure the proposed approach performance. The result shows that learning data by stacking one row at a time is better than learning data matrix sequentially.

Original languageEnglish
Title of host publicationAdvances in Computer Science and Ubiquitous Computing - CSA-CUTE 17
EditorsGangman Yi, Yunsick Sung, James J. Park, Vincenzo Loia
PublisherSpringer Verlag
Pages96-101
Number of pages6
ISBN (Print)9789811076046
DOIs
Publication statusPublished - 2018 Jan 1
EventInternational Conference on Computer Science and its Applications, CSA 2017 - Taichung, Taiwan, Province of China
Duration: 2017 Dec 182017 Dec 20

Publication series

NameLecture Notes in Electrical Engineering
Volume474
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Other

OtherInternational Conference on Computer Science and its Applications, CSA 2017
CountryTaiwan, Province of China
CityTaichung
Period17/12/1817/12/20

Fingerprint

Electroencephalography
Experiments
Deep neural networks

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Cite this

Kwon, Y., Nan, Y., & Kim, S-D. (2018). Transformation of EEG signal for emotion analysis and dataset construction for DNN learning. In G. Yi, Y. Sung, J. J. Park, & V. Loia (Eds.), Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 17 (pp. 96-101). (Lecture Notes in Electrical Engineering; Vol. 474). Springer Verlag. https://doi.org/10.1007/978-981-10-7605-3_16
Kwon, Yeahoon ; Nan, Yiyan ; Kim, Shin-Dug. / Transformation of EEG signal for emotion analysis and dataset construction for DNN learning. Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 17. editor / Gangman Yi ; Yunsick Sung ; James J. Park ; Vincenzo Loia. Springer Verlag, 2018. pp. 96-101 (Lecture Notes in Electrical Engineering).
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Kwon, Y, Nan, Y & Kim, S-D 2018, Transformation of EEG signal for emotion analysis and dataset construction for DNN learning. in G Yi, Y Sung, JJ Park & V Loia (eds), Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 17. Lecture Notes in Electrical Engineering, vol. 474, Springer Verlag, pp. 96-101, International Conference on Computer Science and its Applications, CSA 2017, Taichung, Taiwan, Province of China, 17/12/18. https://doi.org/10.1007/978-981-10-7605-3_16

Transformation of EEG signal for emotion analysis and dataset construction for DNN learning. / Kwon, Yeahoon; Nan, Yiyan; Kim, Shin-Dug.

Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 17. ed. / Gangman Yi; Yunsick Sung; James J. Park; Vincenzo Loia. Springer Verlag, 2018. p. 96-101 (Lecture Notes in Electrical Engineering; Vol. 474).

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

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AB - This work is to design an emotional analysis system using Deep Neural Network based on electroencephalogram data. The data are processed using high pass filtering and removing DC offset method in the proposed system. Then the preprocessed dataset is constructed to analysis the impact of input data placement on recognition performance. In the experiment, the happy and neutral dataset are used to measure the proposed approach performance. The result shows that learning data by stacking one row at a time is better than learning data matrix sequentially.

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Kwon Y, Nan Y, Kim S-D. Transformation of EEG signal for emotion analysis and dataset construction for DNN learning. In Yi G, Sung Y, Park JJ, Loia V, editors, Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 17. Springer Verlag. 2018. p. 96-101. (Lecture Notes in Electrical Engineering). https://doi.org/10.1007/978-981-10-7605-3_16