Emotion recognition from EEG signal enhancing feature map using partial mutual information

被引:3
作者
Akhand, M. A. H. [1 ]
Maria, Mahfuza Akter [1 ]
Kamal, Md Abdus Samad [2 ]
Shimamura, Tetsuya [3 ]
机构
[1] Khulna Univ Engn & Technol, Dept Comp Sci & Engn, Khulna 9203, Bangladesh
[2] Gunma Univ, Grad Sch Sci & Technol, Kiryu 3768515, Japan
[3] Saitama Univ, Grad Sch Sci & Engn, Saitama 3388570, Japan
关键词
Electroencephalography; Emotion; Feature extraction; Connectivity feature map; Convolutional neural network; CONVOLUTIONAL NEURAL-NETWORKS; TOOLBOX;
D O I
10.1016/j.bspc.2023.105691
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The brain signal is the most potent information source to recognize emotion, a fundamental trait of human beings, for providing various emerging personalized courtesies or services instantly to individuals. For emotion recognition (ER), Electroencephalography (EEG) is a preferred brain signal, where the crucial and challenging task is accurately extracting features from complex EEG signals using appropriate computational intelligence or machine learning techniques. Recent ER methods mostly use EEG channel connectivity features to identify the emotion. Specifically, to construct a connectivity feature map (CFM), Pearson correlation coefficient (PCC), mutual information (MI), normalized MI (NMI), and a few other techniques are used. Notably, in the existing ER methods, CFMs are predominantly in the two-dimensional (2D) form, i.e., using the signals from two EEG channels. This study proposes an enhanced CFM that uses partial MI (PMI) by introducing an extra third channel to expose more information and strengthen the feature extraction ability of ER. The proposed technique calculates the PMI-based connectivity features for each pair of EEG channels and presents CFM in 2D and 3D forms. Convolutional Neural Network (CNN) is used to classify emotion using 2D and 3D CFMs. In creating CFMs from EEG signals, rigorous tests have been performed on the DEAP benchmark EEG dataset. As PMI exposed additional information, the enhanced CFM has been found to deliver better ER performances than the one that uses typical MI or NMI, revealing the proposed one outperforming the existing related contemporary methods.
引用
收藏
页数:10
相关论文
共 34 条
[1]  
Adeli H, 2010, AUTOMATED EEG-BASED DIAGNOSIS OF NEUROLOGICAL DISORDERS: INVENTING THE FUTURE OF NEUROLOGY, P119
[2]   Facial Emotion Recognition Using Transfer Learning in the Deep CNN [J].
Akhand, M. A. H. ;
Roy, Shuvendu ;
Siddique, Nazmul ;
Kamal, Md Abdus Samad ;
Shimamura, Tetsuya .
ELECTRONICS, 2021, 10 (09)
[3]  
Akhand MAH., 2021, Deep Learning FundamentalsA Practical Approach to Understanding Deep Learning Methods
[4]   Emotions Recognition Using EEG Signals: A Survey [J].
Alarcao, Soraia M. ;
Fonseca, Manuel J. .
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2019, 10 (03) :374-393
[5]   Fusing highly dimensional energy and connectivity features to identify affective states from EEG signals [J].
Arnau-Gonzalez, Pablo ;
Arevalillo-Herraez, Miguel ;
Ramzan, Naeem .
NEUROCOMPUTING, 2017, 244 :81-89
[6]   Recognition of emotional states using frequency effective connectivity maps through transfer learning approach from electroencephalogram signals [J].
Bagherzadeh, Sara ;
Maghooli, Keivan ;
Shalbaf, Ahmad ;
Maghsoudi, Arash .
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2022, 75
[7]   Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals [J].
Bagherzadeh, Sara ;
Maghooli, Keivan ;
Shalbaf, Ahmad ;
Maghsoudi, Arash .
COGNITIVE NEURODYNAMICS, 2022, 16 (05) :1087-1106
[8]  
Candra H, 2015, IEEE ENG MED BIO, P7250, DOI 10.1109/EMBC.2015.7320065
[9]   Electroencephalograph-Based Emotion Recognition Using Brain Connectivity Feature and Domain Adaptive Residual Convolution Model [J].
Chen, Jingxia ;
Min, Chongdan ;
Wang, Changhao ;
Tang, Zhezhe ;
Liu, Yang ;
Hu, Xiuwen .
FRONTIERS IN NEUROSCIENCE, 2022, 16
[10]  
Chen M, 2015, INT CONF AFFECT, P63, DOI 10.1109/ACII.2015.7344552