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Signal, Image and Video Processing

, Volume 13, Issue 7, pp 1253–1257 | Cite as

Emotion recognition based on sparse learning feature selection method for social communication

  • Yixin Yan
  • Chenyang Li
  • Shaoliang MengEmail author
Original Paper
  • 154 Downloads

Abstract

At present, social communication occupies an increasingly important position in people’s lives. It is especially important to correctly identify other people’s emotional information in social communication. Therefore, emotion recognition is also a hot issue in intelligent information processing. This paper focuses on this problem, mainly based on the feature selection method in emotional recognition of EEG signals. How to select emotion-related features in high-dimensional features is a key factor to achieve fast, accurate and effective identification. This paper proposes a feature selection method based on sparse learning. The method can find a small number of features that contribute to the reconstruction of the category information from the high-dimensional feature space, so as to achieve the purpose of quickly and efficiently acquiring small and emotionally related features. The experimental results show that compared with the traditional method, the proposed method reduces the time consumption of feature selection and obtains a higher emotional five-category correct rate under the same feature dimension.

Keywords

Social communication EEG Emotion recognition Feature selection Sparse learning 

Notes

Acknowledgements

The author wishes to thank all the colleagues for their efforts and efforts in this trial.

Author contributions

Conceptualization: Yixin Yan. Data curation: Yixin Yan, Chenyang Li. Formal analysis: Yixin Yan, Chenyang, Li. Investigation: Yixin Yan. Methodology: Yixin Yan, Chenyang Li. Validation: Yixin Yan, Chenyang, Li. Writing—original draft: Yixin Yan, Chenyang, Li. Writing—review and editing: Yixin Yan, Chenyang, Li.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Electronic Measurement and Control Laboratory, School of Measurement Control and Communication EngineeringHarbin University of Science and TechnologyHarbinChina

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