A Machine Learning Approach to EEG-based Prediction of Human Affective States Using Recursive Feature Elimination Method

被引:1
|
作者
Dadebayev, Didar [1 ]
Wei, Goh Wei [1 ]
Xion, Tan Ee [2 ]
机构
[1] Taylors Univ, Sch Comp Sci & Engn, Subang Jaya, Malaysia
[2] Int Med Univ, Sch Pharm, Life Sci, Bukit Jalil, Malaysia
关键词
D O I
10.1051/matecconf/202133504001
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Emotion recognition, as a branch of affective computing, has attracted great attention in the last decades as it can enable more natural brain-computer interface systems. Electroencephalography (EEG) has proven to be an effective modality for emotion recognition, with which user affective states can be tracked and recorded, especially for primitive emotional events such as arousal and valence. Although brain signals have been shown to correlate with emotional states, the effectiveness of proposed models is somewhat limited. The challenge is improving accuracy, while appropriate extraction of valuable features might be a key to success. This study proposes a framework based on incorporating fractal dimension features and recursive feature elimination approach to enhance the accuracy of EEG-based emotion recognition. The fractal dimension and spectrum-based features to be extracted and used for more accurate emotional state recognition. Recursive Feature Elimination will be used as a feature selection method, whereas the classification of emotions will be performed by the Support Vector Machine (SVM) algorithm. The proposed framework will be tested with a widely used public database, and results are expected to demonstrate higher accuracy and robustness compared to other studies. The contributions of this study are primarily about the improvement of the EEG-based emotion classification accuracy. There is a potential restriction of how generic the results can be as different EEG dataset might yield different results for the same framework. Therefore, experimenting with different EEG dataset and testing alternative feature selection schemes can be very interesting for future work.
引用
收藏
页数:14
相关论文
共 50 条
  • [21] EEG-Based Classification of Spoken Words Using Machine Learning Approaches
    Alonso-Vazquez, Denise
    Mendoza-Montoya, Omar
    Caraza, Ricardo
    Martinez, Hector R.
    Antelis, Javier M.
    COMPUTATION, 2023, 11 (11)
  • [22] Link Prediction in Complex Networks Using Recursive Feature Elimination and Stacking Ensemble Learning
    Wang, Tao
    Jiao, Mengyu
    Wang, Xiaoxia
    ENTROPY, 2022, 24 (08)
  • [23] An EEG-Based Machine-Learning Approach for Stratifying Autism Spectrum Disorder
    Simpraga, Sonja
    Martinez, Erika L. Juarez
    Sprengers, Jan
    Poil, Simon-Shlomo
    Mansvelder, Huibert D.
    Bruining, Hilgo
    Linkenkaer-Hansen, Klaus
    NEUROPSYCHOBIOLOGY, 2018, 77 (03) : 127 - 127
  • [24] EEG-Based Human Emotion Recognition Using Deep Learning
    1600, Institute of Electrical and Electronics Engineers Inc.
  • [25] An Overview of EEG-based Machine Learning Methods in Seizure Prediction and Opportunities for Neurologists in this Field
    Maimaiti, Buajieerguli
    Meng, Hongmei
    Lv, Yudan
    Qiu, Jiqing
    Zhu, Zhanpeng
    Xie, Yinyin
    Li, Yue
    Yu-Cheng
    Zhao, Weixuan
    Liu, Jiayu
    Li, Mingyang
    NEUROSCIENCE, 2022, 481 : 197 - 218
  • [26] EEG-based Golf Putt Outcome Prediction Using Support Vector Machine
    Guo, Qing
    Wu, Jingxian
    Li, Baohua
    2014 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN BRAIN COMPUTER INTERFACES (CIBCI), 2014, : 36 - 42
  • [27] In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method
    Li, Bing-Ke
    Cong, Yong
    Yang, Xue-Gang
    Xue, Ying
    Chen, Yi-Zong
    COMPUTERS IN BIOLOGY AND MEDICINE, 2013, 43 (04) : 395 - 404
  • [28] An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination
    Hakan Gunduz
    Financial Innovation, 7
  • [29] An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination
    Gunduz, Hakan
    FINANCIAL INNOVATION, 2021, 7 (01)
  • [30] EEG-based depression recognition using feature selection method with fuzzy label
    Li, Yalin
    Fang, Yixian
    Ren, Xiuxiu
    Gao, Leiting
    JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2024, 36 (03)