Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27–29, 2023, Tianjin, China

Research Article

Application of EEG-based Machine Learning in Stock Trading-related Emotion Recognition

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  • @INPROCEEDINGS{10.4108/eai.27-10-2023.2342016,
        author={Mingliang  Zuo and Bingbing  Yu and Li  Sui},
        title={Application of EEG-based Machine Learning in Stock Trading-related Emotion Recognition},
        proceedings={Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27--29, 2023, Tianjin, China},
        publisher={EAI},
        proceedings_a={ICEMBDA},
        year={2024},
        month={1},
        keywords={emotion recognition machine learning stock valence-arousal eeg dwt sgd nb lda knn},
        doi={10.4108/eai.27-10-2023.2342016}
    }
    
  • Mingliang Zuo
    Bingbing Yu
    Li Sui
    Year: 2024
    Application of EEG-based Machine Learning in Stock Trading-related Emotion Recognition
    ICEMBDA
    EAI
    DOI: 10.4108/eai.27-10-2023.2342016
Mingliang Zuo1, Bingbing Yu1, Li Sui1,*
  • 1: School of Health Science and Engineering University of Shanghai for Science and Technology
*Contact email: lsui@usst.edu.cn

Abstract

This paper develops a stock emotion recognition system based on a valence/arousal model using electroencephalogram (EEG) signals. The dataset is collected from participants who engage in paper trading using real stock market data, virtual currencies, and emotional outputs. The dataset contains five frequency bands, features such as differential entropy (DE), differential asymmetry (DASM), and rational asymmetry (RASM). Feature selection is performed using mutual information-based filtering combined with chi-square statistics and embedded algorithms in deep learning classifiers. Stock sentiment classification uses established machine learning models, stochastic gradient descent (SGD), linear discriminant analysis (LDA), K nearest neighbor (KNN) and naive Bayes (NB) algorithms. Subsequently, a comprehensive performance analysis and comparative evaluation of each classification algorithm are conducted. Notably, the K-nearest neighbor (KNN) method achieves remarkable accuracy rates of 89.2% for arousal and 94.59% for valence. These results highlight its exceptional performance when compared to pre-existing algorithms applied to stock emotion datasets.