TY - GEN
T1 - Application of EEMD-HHT Method on EEG Analysis for Speech Evoked Emotion Recognition
AU - Chen, Jing
AU - Li, Haifeng
AU - Ma, Lin
AU - Bo, Hongjian
AU - Gao, Xuerong
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - Electroencephalograph (EEG) is widely used to study human brain activities. However, the interpretation of EEG signals is still a challenging computational task. Emerging evidence has shown that the non-stationary traits of EEG signals hinder the way of informative interpretation. Compared to the classical Welch frequency analysis method (short-Time Fourier transform), Hilbert Huang Transform(HHT) is more suitable for non-linear and non-stationary signals. This paper proposes a band energy extraction method based on EEMD-HHT for time-frequency analysis of EEG signals. We evaluate the method on an EEG database obtained through the emotional cognitive experiment. The auditory stimulus in this paper are selected from CHEAVD2 which is a speech emotion database of the Chinese Academy of Sciences. The correlation coefficients between the predict and target values reach 0.51 and 0.43 for arousal and valence dimension, respectively. This method shows great potentials in applications of computational neuroscience and cognition of art creation.
AB - Electroencephalograph (EEG) is widely used to study human brain activities. However, the interpretation of EEG signals is still a challenging computational task. Emerging evidence has shown that the non-stationary traits of EEG signals hinder the way of informative interpretation. Compared to the classical Welch frequency analysis method (short-Time Fourier transform), Hilbert Huang Transform(HHT) is more suitable for non-linear and non-stationary signals. This paper proposes a band energy extraction method based on EEMD-HHT for time-frequency analysis of EEG signals. We evaluate the method on an EEG database obtained through the emotional cognitive experiment. The auditory stimulus in this paper are selected from CHEAVD2 which is a speech emotion database of the Chinese Academy of Sciences. The correlation coefficients between the predict and target values reach 0.51 and 0.43 for arousal and valence dimension, respectively. This method shows great potentials in applications of computational neuroscience and cognition of art creation.
KW - Computational neuroscience
KW - Electro encephalograph (EEG)
KW - Ensemble Empirical Mode Decomposition(EEMD)
KW - Hilbert-Huang Transform (HHT)
KW - Time frequency analysis
UR - https://www.scopus.com/pages/publications/85092192611
U2 - 10.1109/MIPR49039.2020.00082
DO - 10.1109/MIPR49039.2020.00082
M3 - 会议稿件
AN - SCOPUS:85092192611
T3 - Proceedings - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020
SP - 376
EP - 381
BT - Proceedings - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020
Y2 - 6 August 2020 through 8 August 2020
ER -