Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection

被引:6
|
作者
Li, Gen [1 ]
Jung, Jason J. [1 ]
机构
[1] Chung Ang Univ, Dept Comp Engn, 84 Heukseok Ro, Seoul 06974, South Korea
来源
APPLIED SCIENCES-BASEL | 2020年 / 10卷 / 21期
基金
新加坡国家研究基金会;
关键词
signal preprocessing; signal similarity; emotion detection; RECOGNITION;
D O I
10.3390/app10217677
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Emotion detection is an important research issue in electroencephalogram (EEG). Signal preprocessing and feature selection are parts of feature engineering, which determines the performance of emotion detection and reduces the training time of the deep learning models. To select the efficient features for emotion detection, we propose a maximum marginal approach on EEG signal preprocessing. The approach selects the least similar segments between two EEG signals as features that can represent the difference between EEG signals caused by emotions. The method defines a signal similarity described as the distance between two EEG signals to find the features. The frequency domain of EEG is calculated by using a wavelet transform that exploits a wavelet to calculate EEG components in a different frequency. We have conducted experiments by using the selected feature from real EEG data recorded from 10 college students. The experimental results show that the proposed approach performs better than other feature selection methods by 17.9% on average in terms of accuracy. The maximum marginal approach-based models achieve better performance than the models without feature selection by 21% on average in terms of accuracy.
引用
收藏
页码:1 / 11
页数:11
相关论文
共 50 条
  • [1] EEG signal preprocessing for biometric recognition
    Maiorana, Emanuele
    Sole-Casals, Jordi
    Campisi, Patrizio
    MACHINE VISION AND APPLICATIONS, 2016, 27 (08) : 1351 - 1360
  • [2] EEG signal preprocessing for biometric recognition
    Emanuele Maiorana
    Jordi Solé-Casals
    Patrizio Campisi
    Machine Vision and Applications, 2016, 27 : 1351 - 1360
  • [3] Emotion Stress Detection using EEG Signal and Deep Learning Technologies
    Liao, Chung-Yen
    Chen, Rung-Ching
    Tai, Shao-Kuo
    PROCEEDINGS OF 4TH IEEE INTERNATIONAL CONFERENCE ON APPLIED SYSTEM INNOVATION 2018 ( IEEE ICASI 2018 ), 2018, : 90 - 93
  • [4] Comparison of EEG Signal Preprocessing Methods for SSVEP Recognition
    Kolodziej, Marcin
    Majkowski, Andrzej
    Oskwarek, Lukasz
    Rak, Remigiusz J.
    2016 39TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP), 2016, : 340 - 345
  • [5] Robust Common Spatial Patterns for EEG Signal Preprocessing
    Yong, Xinyi
    Ward, Rabab K.
    Birch, Gary E.
    2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8, 2008, : 2087 - +
  • [6] Emotion Detection in Infants' Cries Based on a Maximum Likelihood Approach
    Matsunaga, S.
    Sakaguchi, S.
    Yamashita, M.
    Miyahara, S.
    Nishitani, S.
    Shinohara, K.
    INTERSPEECH 2006 AND 9TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING, VOLS 1-5, 2006, : 1834 - +
  • [7] New approach in features extraction for EEG signal detection
    Guerrero-Mosquera, Carlos
    Navia Vazquez, Angel
    2009 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-20, 2009, : 13 - 16
  • [8] A Novel Approach for Emotion Recognition Based on EEG Signal Using Deep Learning
    Abdulrahman, Awf
    Baykara, Muhammet
    Alakus, Talha Burak
    APPLIED SCIENCES-BASEL, 2022, 12 (19):
  • [9] EEG Emotion Detection Review
    Christensen, Lars Rune
    Abdullah, Mohamed Ahmed
    2018 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (CIBCB), 2018, : 125 - 131
  • [10] EMOTION DETECTION IN INFANT EEG
    Bolinger, Elaina
    Matuz, Tamara
    Hettich, Dirk T.
    Born, Jan
    Birbaumer, Niels
    PSYCHOPHYSIOLOGY, 2013, 50 : S49 - S49