Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (12): 312-318.doi: 10.11896/jsjkx.201000141

• Artificial Intelligence • Previous Articles     Next Articles

EEG Emotion Recognition Based on Frequency and Channel Convolutional Attention

CHAI Bing1,2, LI Dong-dong1,2, WANG Zhe1, GAO Da-qi1   

  1. 1 School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China
    2 Provincial Key Laboratory of Computer Information Processing Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Received:2020-10-24 Revised:2021-03-20 Online:2021-12-15 Published:2021-11-26
  • About author:CHAI Bing,born in 1996,postgraduate,is a member of China Computer Federation.Her main research interests include deep learning and emotion recognition.
    GAO Da-qi,born in 1957,Ph.D,professor.His main research interests include machine learning and pattern recognition.
  • Supported by:
    National Natural Science Foundation of China(61806078,62076094,61976091),“Shuguang Program” Supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission(61725301),National Major Scientific and Technological Special Project(2019ZX09201004) and Shanghai Science and Technology Program(20511100600).

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Abstract: The existing emotion recognition researches generally use neural network and attention mechanism to learn emotional features,which have relatively single feature representation.Moreover,neuroscience studies have shown that EEG signals of different frequencies and channels have different responses to emotion.Therefore,this paper proposes a method of fusing frequency and electrode channel convolutional attention for EEG emotion recognition.Specifically,EEG signals are firstly decomposed into different frequency bands and the corresponding frame-level features are extracted.Then the pre-activated residual network is employed to learn deep emotion-relevant features.At the same time,the frequency and electrode channel convolutional attention module is integrated into each pre-activated residual unit of residual network to model the frequency and channel information of EEG signals,thus generating final representation of EEG features.Experiments on DEAP and DREAMER datasets show that the proposed method helps to enhance the importing of emotion-salient information in EEG signals when compared with single-layer attention mechanism,and generates better recognition performance.

Key words: EEG emotion recognition, Feature representation, Frequency and electrode channel convolutional attention, Pre-activated residual unit, Residual network

CLC Number: 

  • TP301
[1]JAIN D,SHAMSOLMOALI P,SEHDEV P.Extended deep neural network for facial emotion recognition[J].Pattern Recognition Letters,2019,120:69-74.
[2]LIU Z,WU M,CAO W,et al.Speech emotion recognition based on feature selection and extreme learning machine decision tree[J].Neurocomputing,2018,273:271-280.
[3]GU Y,CHEN S,MARSIC I.Deep multimodal learning for emotion recognition in spoken language[C]//IEEE International Conference on Acoustics,Speech and Signal Processing.Calgary:IEEE Press,2018:5079-5083.
[4]ALARCAO S,FONSECA M.Emotions recognition using EEG signals:A survey[J].IEEE Transactions on Affective Computing,2019,10(3):374-393.
[5]HAMADA M,ZAIDAN B,ZAIDAN A.A systematic review for human EEG brain signals based emotion classification,feature extraction,brain condition,group comparison[J].Journal of Medical Systems,2018,42(9):1-25.
[6]KNYAZEV G G.EEG delta oscillations as a correlate of basic homeostatic and motivational processes[J].Neuroscience & Biobehavioral Reviews,2012,36(1):677-695.
[7]SANDE M C,KNUT E,PETER H,et al.EEG theta power is an early marker of cognitive decline in dementia due to Alzheimer's disease[J].Journal of Alzheimer's Disease,2018,64(4):1359-1371.
[8]FINK A,ROMINGER C,BENEDEK M,et al.EEG alpha activity during imagining creative moves in soccer decision-making situations[J].Neuropsychologia,2018,114:118-124.
[9]GOLA M,MAGNUSKI M,SZUMSKA I,et al.EEG beta band activity is related to attention and attentional deficits in the visualperformance of elderly subjects[J].International Journal of Psychophysiology,2013,89(3):334-341.
[10]MAFFEI A,SPIRONELLI C,ANGRILLI A.Affective and cor- tical EEG gamma responses to emotional movies in women with high vs low traits of empathy[J].Neuropsychologia,2019,133:107175.
[11]HADJIDIMITRIOU S,HADJILEONTIADIS L.EEG-based classification of music appraisal responses using time-frequency analysis and familiarity ratings[J].IEEE Transactions on Affective Computing,2013,4(2):161-172.
[12]MOHAMMADI Z,FROUNCHI J,AMIRI M.Wavelet-based emotion recognition system using EEG signal[J].Neural Computing and Applications,2017,28(8):1985-1990.
[13]PIHO L,TJAHJADI T.A mutual information based adaptive windowing of informative EEG for emotion recognition[J].IEEE Transactions on Affective Computing,2020,11(4):722-735.
[14]TRIPATHI S,ACHARYA S,SHARMA R,et al.Using deep and convolutional neural networks for accurate emotion classification on DEAP dataset[C]//Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.IEEE Press,2017:4746-4752.
[15]LI Y,ZHENG W,ZONG Y,et al.A bi-hemisphere domain adversarial neural network model for EEG emotion recognition[J].IEEE Transactions on Affective Computing,2021,12(2):494-504.
[16]SONG T,ZHENG W,SONG P,et al.EEG emotion recognition using dynamical graph convolutional neural networks[J].IEEE Transactions on Affective Computing,2020,11(3):532-541.
[17]PANDEY P,SEEJA K.Subject independent emotion recognition from EEG using VMD and deep learning[J].Journal of King Saud University-Computer and Information Sciences,2019,https://doi.org/10.1016/j.jksuci.2019.11.003.
[18]SHARMA R,PACHORI R,SIRCAR P.Automated emotion recognition based on higher order statistics and deep learning algorithm[J].Biomedical Signal Processing and Control,2020,58:101867.
[19]ZHANG D,YAO L,CHEN K,et al.A convolutional recurrent attention model for subject-independent EEG signal analysis[J].IEEE Signal Processing Letters,2019,26(5):715-719.
[20]CHEN J,JIANG D,ZHANG Y.A hierarchical bidirectional GRU model with attention for EEG-based emotion classification[J].IEEE Access,2019,7:118530-118540.
[21]QIU J,LI X,HU K.Correlated attention networks for multimodal emotion recognition[C]//IEEE International Conference on Bioinformatics and Biomedicine.IEEE Press,2018:2656-2660.
[22]DUAN R,ZHU J,LU B.Differential entropy feature for EEG-based emotion classification[C]//2013 6th International IEEE/EMBS Conference on Neural Engineering(NER).IEEE Press,2013:81-84.
[23]WOO S,PARK J,LEE J.CBAM:Convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018.
[24]HE K,ZHANG X,REN S,et al.Identity mappings in deep residual networks[C]//European Conference on Computer Vision.Cham:Springer,2016:630-645.
[25]KOELSTRA S,MUHL C,SOLEYMANI M,et al.DEAP:A database for emotion analysis using physiological signals[J].IEEE Transactions on Affective Computing,2011,3(1):18-31.
[26]KATSIGIANNIS S,RAMZAN N.Dreamer:A database for emotion recognition through EEG and ECG signals from wire-less low-cost off-the-shelf devices[J].IEEE Journal of Biome-dical and Health Informatics,2017,22(1):98-107.
[27]ROZGIC V,VITALADEVUNI S,PRASAD R.Robust EEG emotion classification using segment leveldecision fusion[C]//IEEE International Conference on Acoustics,Speech and Signal Processing.Vancouver:IEEE Press,2013:1286-1290.
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