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

Yeahoon Kwon; Yiyan Nan; Shin Dug Kim

doi:10.1007/978-981-10-7605-3_16

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

Yeahoon Kwon, Yiyan Nan, Shin Dug Kim

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

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 language	English
Title of host publication	Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 17
Editors	Gangman Yi, Yunsick Sung, James J. Park, Vincenzo Loia
Publisher	Springer Verlag
Pages	96-101
Number of pages	6
ISBN (Print)	9789811076046
DOIs	https://doi.org/10.1007/978-981-10-7605-3_16
Publication status	Published - 2018
Event	International Conference on Computer Science and its Applications, CSA 2017 - Taichung, Taiwan, Province of China Duration: 2017 Dec 18 → 2017 Dec 20

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	474
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Other

Other	International Conference on Computer Science and its Applications, CSA 2017
Country/Territory	Taiwan, Province of China
City	Taichung
Period	17/12/18 → 17/12/20

Bibliographical note

Funding Information:
Acknowledgements. This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (R0124-16-0002, Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly).

Funding Information:
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (R0124-16-0002, Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly).

Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2018.

All Science Journal Classification (ASJC) codes

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-10-7605-3_16

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

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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.",

author = "Yeahoon Kwon and Yiyan Nan and Kim, {Shin Dug}",

note = "Funding Information: Acknowledgements. This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (R0124-16-0002, Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly). Funding Information: This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (R0124-16-0002, Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly). Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2018.; International Conference on Computer Science and its Applications, CSA 2017 ; Conference date: 18-12-2017 Through 20-12-2017",

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Kwon, Y, Nan, Y & Kim, SD 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 proceeding › Conference contribution

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T1 - Transformation of EEG signal for emotion analysis and dataset construction for DNN learning

AU - Kwon, Yeahoon

AU - Nan, Yiyan

AU - Kim, Shin Dug

N1 - Funding Information: Acknowledgements. This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (R0124-16-0002, Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly). Funding Information: This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (R0124-16-0002, Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly). Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2018.

PY - 2018

Y1 - 2018

N2 - 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.

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

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