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Experimental research on emotion recognition based on brain-computer interface and brain waves

Authors:

Zhang Jingru,

Qunyong Yuan,

Nanfeng XiaoAuthors Info & Claims

AIPR '19: Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition

Pages 50 - 55

https://doi.org/10.1145/3357254.3357272

Published: 16 August 2019 Publication History

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Abstract

Emotion recognition and classification are important research contents in the field of emotional computing. The current research focuses on the visual field and the speech field, but the accuracy of the emotion recognition and the classification which can be achieved so far is low, which is not enough for commercial applications. At present, due to the rapid progress in research on the brain waves and the brain-computer interfaces, and the great application value in the fields of the medicine and the military, this paper uses the brain electrode caps to collect the brain waves of the human brains under the seven different emotional states. The brain-computer interface transmits the brain wave patterns and the data to the computer, observes the brain waves in the OpenBCI_GUI graphical interface and records the changes in real time. After obtaining the brainwave data under the different emotional states, this paper uses the three statistical methods, such as the AdaBoosting algorithm, to perform the emotional classification on the recorded brainwave data. The experimental results show that the classification effect is good.

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Cited By

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Sapozhnikova KTaymanov RBaksheeva IDanilova I(2022)Measurements as the basis for interpreting the content of emotionally coloured acoustic signalsMeasurement10.1016/j.measurement.2022.111861202(111861)Online publication date: Oct-2022
https://doi.org/10.1016/j.measurement.2022.111861
Dadebayev DGoh WTan E(2022)EEG-based emotion recognitionJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2021.03.00934:7(4385-4401)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.jksuci.2021.03.009
Abdeltawab AAhmad A(2020)Classification of Motor Imagery EEG Signals Using Machine Learning2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)10.1109/ICSET51301.2020.9265364(196-201)Online publication date: 9-Nov-2020
https://doi.org/10.1109/ICSET51301.2020.9265364

Index Terms

Experimental research on emotion recognition based on brain-computer interface and brain waves
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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Published In

AIPR '19: Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition

August 2019

198 pages

ISBN:9781450372299

DOI:10.1145/3357254

Conference Chairs:
Li Ma
North China University of Technology, China
,
Xu Huang
University of Canberra, Australia

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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Association for Computing Machinery

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Published: 16 August 2019

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National Natural Science Foundation of China
Natural Science Foundation of Guangdong Province

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AIPR 2019

AIPR 2019: 2019 2nd International Conference on Artificial Intelligence and Pattern Recognition

August 16 - 18, 2019

Beijing, China

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Sapozhnikova KTaymanov RBaksheeva IDanilova I(2022)Measurements as the basis for interpreting the content of emotionally coloured acoustic signalsMeasurement10.1016/j.measurement.2022.111861202(111861)Online publication date: Oct-2022
https://doi.org/10.1016/j.measurement.2022.111861
Dadebayev DGoh WTan E(2022)EEG-based emotion recognitionJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2021.03.00934:7(4385-4401)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.jksuci.2021.03.009
Abdeltawab AAhmad A(2020)Classification of Motor Imagery EEG Signals Using Machine Learning2020 IEEE 10th International Conference on System Engineering and Technology (ICSET)10.1109/ICSET51301.2020.9265364(196-201)Online publication date: 9-Nov-2020
https://doi.org/10.1109/ICSET51301.2020.9265364

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Survey on Brain-Computer Interface: An Emerging Computational Intelligence Paradigm

Brain-computer interface research at Katholieke Universiteit Leuven

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