Review Article
Open Access
Issue
Published:
04 February 2021
Open Access
Issue
Published:
04 February 2021
Video-triggered EEG-emotion public databases and current methods: A survey
Zhen Liang1,2(
)
)
Cite this article:
Hu W, Huang G, Li L, et al.
Video-triggered EEG-emotion public databases and current methods: A survey.
Brain Science Advances,
2020, 6(3): 255-287.
https://doi.org/10.26599/BSA.2020.9050026
Download citation
1288
Views
170
Downloads
Citations
30
Crossref
N/A
WoS
N/A
Scopus
N/A
CSCD
Emotions, formed in the process of perceiving external environment, directly affect human daily life, such as social interaction, work efficiency, physical wellness, and mental health. In recent decades, emotion recognition has become a promising research direction with significant application values. Taking the advantages of electroencephalogram (EEG) signals (i.e., high time resolution) and video-based external emotion evoking (i.e., rich media information), video-triggered emotion recognition with EEG signals has been proven as a useful tool to conduct emotion-related studies in a laboratory environment, which provides constructive technical supports for establishing real-time emotion interaction systems. In this paper, we will focus on video-triggered EEG-based emotion recognition and present a systematical introduction of the current available video-triggered EEG-based emotion databases with the corresponding analysis methods. First, current video-triggered EEG databases for emotion recognition (e.g., DEAP, MAHNOB-HCI, SEED series databases) will be presented with full details. Then, the commonly used EEG feature extraction, feature selection, and modeling methods in video-triggered EEG-based emotion recognition will be systematically summarized and a brief review of current situation about video-triggered EEG-based emotion studies will be provided. Finally, the limitations and possible prospects of the existing video-triggered EEG-emotion databases will be fully discussed.
menu
Abstract
Full text
Outline
About this article
Video-triggered EEG-emotion public databases and current methods: A survey
Zhen Liang1,2(
)
)
Abstract
Emotions, formed in the process of perceiving external environment, directly affect human daily life, such as social interaction, work efficiency, physical wellness, and mental health. In recent decades, emotion recognition has become a promising research direction with significant application values. Taking the advantages of electroencephalogram (EEG) signals (i.e., high time resolution) and video-based external emotion evoking (i.e., rich media information), video-triggered emotion recognition with EEG signals has been proven as a useful tool to conduct emotion-related studies in a laboratory environment, which provides constructive technical supports for establishing real-time emotion interaction systems. In this paper, we will focus on video-triggered EEG-based emotion recognition and present a systematical introduction of the current available video-triggered EEG-based emotion databases with the corresponding analysis methods. First, current video-triggered EEG databases for emotion recognition (e.g., DEAP, MAHNOB-HCI, SEED series databases) will be presented with full details. Then, the commonly used EEG feature extraction, feature selection, and modeling methods in video-triggered EEG-based emotion recognition will be systematically summarized and a brief review of current situation about video-triggered EEG-based emotion studies will be provided. Finally, the limitations and possible prospects of the existing video-triggered EEG-emotion databases will be fully discussed.
Keywords:
emotion recognition, EEG signals, video-triggered, emotion database
References(131)
[2]
RS Bucks, SA Radford. Emotion processing in Alzheimer's disease. Aging Ment Health. 2004, 8(3): 222-232.
[3]
BC Sirois, MM Burg. Negative emotion and coronary heart disease. Behav Modif. 2003, 27(1): 83-102.
[4]
J Joormann, IH Gotlib. Emotion regulation in depression: relation to cognitive inhibition. Cogn Emot. 2010, 24(2): 281-298.
[5]
R Plutchik. The nature of emotions: Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. Am Sci. 2001, 89(4): 344-350.
[6]
J Suttles, N Ide. Distant supervision for emotion classification with discrete binary values. In Computational Linguistics and Intelligent Text Processing. A Gelbukh, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
[7]
L Shu, JY Xie, MY Yang, et al. A review of emotion recognition using physiological signals. Sensors. 2018, 18(7): E2074.
[8]
P Ekman, WV Friesen, M O'Sullivan, et al. Universals and cultural differences in the judgments of facial expressions of emotion. J Pers Soc Psychol. 1987, 53(4): 712-717.
[9]
GK Verma, US Tiwary. Affect representation and recognition in 3D continuous valence-arousal- dominance space. Multimed Tools Appl. 2017, 76(2): 2159-2183.
[10]
JA Russell. A circumplex model of affect. J Pers Soc Psychol. 1980, 39(6): 1161-1178.
[11]
Y Liu, O Sourina. EEG-based dominance level recognition for emotion-enabled interaction. In 2012 IEEE International Conference on Multimedia and Expo, Melbourne, VIC, Australia, 2012, pp 1039-1044.
[12]
SM Alarcão, MJ Fonseca. Emotions recognition using EEG signals: a survey. IEEE Trans Affect Comput. 2019, 10(3): 374-393.
[13]
A Etkin, C Büchel, JJ Gross. The neural bases of emotion regulation. Nat Rev Neurosci. 2015, 16(11): 693-700.
[14]
G Chanel, JJM Kierkels, M Soleymani, et al. Short-term emotion assessment in a recall paradigm. Int J Hum-Comput Stud. 2009, 67(8): 607-627.
[15]
N Zhuang, Y Zeng, K Yang, et al. Investigating patterns for self-induced emotion recognition from EEG signals. Sensors. 2018, 18(3): 841.
[16]
RM Mehmood, HJ Lee. A novel feature extraction method based on late positive potential for emotion recognition in human brain signal patterns. Comput Electr Eng. 2016, 53: 444-457.
[17]
AT Sohaib, S Qureshi, J Hagelbäck, et al. Evaluating classifiers for emotion recognition using EEG. In Foundations of Augmented Cognition. DD Schmorrow, CM Fidopiastis, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp 492-501.
[18]
YP Lin, CH Wang, TP Jung, et al. EEG-based emotion recognition in music listening. IEEE Trans Biomed Eng. 2010, 57(7): 1798-1806.
[19]
I Daly, D Williams, J Hallowell, et al. Music-induced emotions can be predicted from a combination of brain activity and acoustic features. Brain Cogn. 2015, 101: 1-11.
[20]
S Koelstra, C Muhl, M Soleymani, et al. DEAP: a database for emotion Analysis: Using physiological signals. IEEE Trans Affective Comput. 2012, 3(1): 18-31.
[21]
WL Zheng, W Liu, YF Lu, et al. EmotionMeter: a multimodal framework for recognizing human emotions. IEEE Trans Cybern. 2019, 49(3): 1110-1122.
[22]
S Katsigiannis, N Ramzan. DREAMER: a database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE J Biomed Health Inform. 2018, 22(1): 98-107.
[23]
WL Zheng, BL Lu. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans Auton Mental Dev. 2015, 7(3): 162-175.
[24]
CA Kothe, S Makeig, JA Onton. Emotion recognition from EEG during self-paced emotional imagery. In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland, 2013, pp 855-858.
[25]
CE Salas, D Radovic, OH Turnbull. Inside-out: Comparing internally generated and externally generated basic emotions. Emotion. 2012, 12(3): 568-578.
[26]
S Poria, E Cambria, R Bajpai, et al. A review of affective computing: From unimodal analysis to multimodal fusion. Inf Fusion. 2017, 37: 98-125.
[27]
KK Ellard, TJ Farchione, DH Barlow. Relative effectiveness of emotion induction procedures and the role of personal relevance in a clinical sample: a comparison of film, images, and music. J Psychopathol Behav Assess. 2012, 34(2): 232-243.
[28]
M Soleymani, S Asghari-Esfeden, Y Fu, et al. Analysis of EEG signals and facial expressions for continuous emotion detection. In IEEE Trans Affective Comput. 2015, 7(1): 17-28.
[29]
G Castellano, L Kessous, G Caridakis. Emotion recognition through multiple modalities: face, body gesture, speech. In Affect and Emotion in Human- Computer Interaction. C Peter, R Beale, Eds. Berlin, Heidelberg: Springer, 2008.
[30]
L Kessous, G Castellano, G Caridakis. Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis. J Multimodal User Interfaces. 2010, 3(1/2): 33-48.
[31]
H Gunes, B Schuller, M Pantic, et al. Emotion representation, analysis and synthesis in continuous space: a survey. In 2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG), Santa Barbara, CA, USA, 2011, pp 827-834.
[32]
CL Bethel, K Salomon, RR Murphy, et al. Survey of Psychophysiology Measurements Applied to Human- Robot Interaction. In RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication, Jeju, South Korea, 2007, pp 732-737.
[33]
MK Abadi, R Subramanian, SM Kia, et al. DECAF: MEG-based multimodal database for decoding affective physiological responses. IEEE Trans Affective Comput. 2015, 6(3): 209-222.
[34]
R Khosrowabadi, HC Quek, A Wahab, et al. EEG-based Emotion Recognition Using Self- Organizing Map for Boundary Detection. In 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey, 2010, pp 4242-4245.
[35]
CL Lisetti, F Nasoz. Using noninvasive wearable computers to recognize human emotions from physiological signals. EURASIP J Adv Signal Process. 2004, 2004(11): 929414.
[36]
JA Healey, RW Picard. Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans Intell Transport Syst. 2005, 6(2): 156-166.
[37]
SD Kreibig. Autonomic nervous system activity in emotion: a review. Biol Psychol. 2010, 84(3): 394-421.
[38]
F Agrafioti, D Hatzinakos, AK Anderson. ECG pattern analysis for emotion detection. IEEE Trans Affective Comput. 2012, 3(1): 102-115.
[39]
P Das, A Khasnobish, DN Tibarewala. Emotion recognition employing ECG and GSR signals as markers of ANS. In 2016 Conference on Advances in Signal Processing (CASP), Pune, India, 2016, pp 37-42.
[40]
Q Zhang, XX Chen, QY Zhan, et al. Respiration- based emotion recognition with deep learning. Comput Ind. 2017, 92: 84-90.
[41]
D Nie, XW Wang, RN Duan, et al. A survey on EEG based emotion recognition. Chinese Journal of Biomedical Engineering. 2012, 31(4): 595-606.
[42]
SH Fairclough. Fundamentals of physiological computing. Interact Comput. 2009, 21(1/2): 133-145.
[43]
CE Waugh, EZ Shing, BM Avery. Temporal dynamics of emotional processing in the brain. Emot Rev. 2015, 7(4): 323-329.
[44]
R Thiruchselvam, J Blechert, G Sheppes, et al. The temporal dynamics of emotion regulation: an EEG study of distraction and reappraisal. Biol Psychol. 2011, 87(1): 84-92.
[45]
IB Mauss, MD Robinson. Measures of emotion: a review. Cogn Emot. 2009, 23(2): 209-237.
[46]
PR Davidson, RD Jones, MT Peiris. EEG-based lapse detection with high temporal resolution. IEEE Trans Biomed Eng. 2007, 54(5): 832-839.
[47]
CP Niemic. Studies of emotion: A theoretical and empirical review of psychophysiological studies of emotion. 2002, 1(1): 15-18.
[48]
T Alotaiby, FEA El-Samie, SA Alshebeili, et al. A review of channel selection algorithms for EEG signal processing. EURASIP J Adv Signal Process. 2015, 2015: 66.
[49]
RJ Davidson, H Abercrombie, JB Nitschke, et al. Regional brain function, emotion and disorders of emotion. Curr Opin Neurobiol. 1999, 9(2): 228-234.
[50]
ME Raichle, AM MacLeod, AZ Snyder, et al. A default mode of brain function. PNAS. 2001, 98(2): 676-682.
[51]
B Güntekin, E Basar. Emotional face expressions are differentiated with brain oscillations. Int J Psychophysiol. 2007, 64(1): 91-100.
[52]
XW Wang, D Nie, BL Lu. Emotional state classification from EEG data using machine learning approach. Neurocomputing. 2014, 129: 94-106.
[53]
LA Schmidt, LJ Trainor. Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions. Cogn Emot. 2001, 15(4): 487-500.
[54]
DJLG Schutter, P Putman, E Hermans, et al. Parietal electroencephalogram beta asymmetry and selective attention to angry facial expressions in healthy human subjects. Neurosci Lett. 2001, 314(1/2): 13-16.
[55]
PJ Lang, MM Bradley. Emotion and the motivational brain. Biol Psychol. 2010, 84(3): 437-450.
[56]
MK Kim, M Kim, E Oh, et al. A review on the computational methods for emotional state estimation from the human EEG. Comput Math Methods Med. 2013, 2013: 1-13.
[57]
C Mühl, B Allison, A Nijholt, et al. A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges. Brain - Comput Interfaces. 2014, 1(2): 66-84.
[58]
W Ray, H Cole. EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes. Science. 1985, 228(4700): 750-752.
[59]
R Gordon, J Ciorciari, T van Laer. Using EEG to examine the role of attention, working memory, emotion, and imagination in narrative transportation. Eur J Marketing. 2017, 52(1/2):92-117.
[60]
TA Pedley. Electroencephalography: Basic principles, clinical applications, and related fields. E Niedermeyer, FL da Silva, Eds. Baltimore: Williams & Wilkins, 2005.
[61]
E Bonanni, E di Coscio, M Maestri, et al. Differences in EEG delta frequency characteristics and patterns in slow-wave sleep between dementia patients and controls. J Clin Neurophysiol. 2012, 29(1): 50-54.
[62]
JJ Palop, L Mucke. Epilepsy and cognitive impairments in Alzheimer disease. Arch Neurol. 2009, 66(4): 435-440.
[63]
D Sammler, M Grigutsch, T Fritz, et al. Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music. Psychophysiology. 2007, 44(2): 293-304.
[64]
LA Finelli, H Baumann, AA Borbély, et al. Dual electroencephalogram markers of human sleep homeostasis: correlation between theta activity in waking and slow-wave activity in sleep. Neuroscience. 2000, 101(3): 523-529.
[65]
M Kawasaki, K Kitajo, Y Yamaguchi. Dynamic links between theta executive functions and alpha storage buffers in auditory and visual working memory. Eur J Neurosci. 2010, 31(9): 1683-1689.
[66]
OM Bazanova, D Vernon. Interpreting EEG alpha activity. Neurosci Biobehav Rev. 2014, 44: 94-110.
[67]
D Choi, T Sekiya, N Minote, et al. Relative left frontal activity in reappraisal and suppression of negative emotion: Evidence from frontal alpha asymmetry (FAA). Int J Psychophysiol. 2016, 109: 37-44.
[68]
BD Nelson, EM Kessel, DN Klein, et al. Depression symptom dimensions and asymmetrical frontal cortical activity while anticipating reward. Psychophysiology. 2018, 55(1): .
[69]
R Yuvaraj, M Murugappan, NM Ibrahim, et al. Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative study. J Integr Neurosci. 2014, 13(1): 89-120.
[70]
M Gola, M Magnuski, I Szumska, et al. EEG beta band activity is related to attention and attentional deficits in the visual performance of elderly subjects. Int J Psychophysiol. 2013, 89(3): 334-341.
[71]
MM Müller, A Keil, T Gruber, et al. Processing of affective pictures modulates right-hemispheric gamma band EEG activity. Clin Neurophysiol. 1999, 110(11): 1913-1920.
[72]
D Huang, CT Guan, KK Ang, et al. Asymmetric Spatial Pattern for EEG-based emotion detection. In The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia, 2012, pp 1-7.
[73]
K Park, H Choi, KJ Lee, et al. Emotion recognition based on the asymmetric left and right activation. Int J Med Med Sci. 2011, 3(6): 201-209.
[74]
PJ Lang, MM Bradley, BN Cuthbert. International Affective Picture System (IAPS): Affective ratings of pictures and instruction manual. Technical Report A-8. University of Florida, Gainesville, FL, USA, 2008.
[75]
A Marchewka, Ł Zurawski, K Jednoróg, et al. The Nencki Affective Picture System (NAPS): introduction to a novel, standardized, wide-range, high- quality, realistic picture database. Behav Res Methods. 2014, 46(2): 596-610.
[76]
MM Bradley, PJ Lang. The International Affective Digitized Sounds (2nd Edition; IADS-2): Affective ratings of sounds and instruction manual. Technical report B-3. University of Florida, Gainesville, Fl, USA, 2008.
[77]
M Soleymani, J Lichtenauer, T Pun, et al. A multimodal database for affect recognition and implicit tagging. IEEE Trans Affective Comput. 2012, 3(1): 42-55.
[78]
H Becker, J Fleureau, P Guillotel, et al. Emotion recognition based on high-resolution EEG recordings and reconstructed brain sources. IEEE Trans Affective Comput. 2020, 11(2): 244-257.
[79]
TF Song, WM Zheng, C Lu, et al. MPED: a multi-modal physiological emotion database for discrete emotion recognition. IEEE Access. 2019, 7: 12177-12191.
[80]
Y Li, WM Zheng, Z Cui, et al. EEG emotion recognition based on graph regularized sparse linear regression. Neural Process Lett. 2019, 49(2): 555-571.
[81]
YJ Liu, MJ Yu, GZ Zhao, et al. Real-time movie-induced discrete emotion recognition from EEG signals. IEEE Trans Affective Comput. 2018, 9(4): 550-562.
[82]
WL Zheng, JY Zhu, BL Lu. Identifying stable patterns over time for emotion recognition from EEG. IEEE Trans Affective Comput. 2019, 10(3): 417-429.
[83]
JY Zhu, WL Zheng, BL Lu. Cross-subject and cross-gender emotion classification from EEG. In World Congress on Medical Physics and Biomedical Engineering, Toronto, Canada, 2015, pp 1188-1191.
[84]
WL Zheng, BL Lu. A multimodal approach to estimating vigilance using EEG and forehead EOG. J Neural Eng. 2017, 14(2): 026017.
[85]
S Tripathi, S Acharya, RD Sharma, et al. Using deep and convolutional neural networks for accurate emotion classification on deap dataset. In Twenty- Ninth IAAI Conference, San Francisco, California, USA, 2017, pp 4746-4752.
[86]
Z Liang, S Oba, S Ishii. An unsupervised EEG decoding system for human emotion recognition. Neural Netw. 2019, 116: 257-268.
[87]
X Chai, QS Wang, YP Zhao, et al. Unsupervised domain adaptation techniques based on auto-encoder for non-stationary EEG-based emotion recognition. Comput Biol Med. 2016, 79: 205-214.
[89]
E Kroupi, A Yazdani, T Ebrahimi. EEG correlates of different emotional states elicited during watching music videos. In International Conference on Affective Computing and Intelligent Interaction, Berlin, Heidelberg, 2011, pp 457-466.
[90]
JX Liu, HY Meng, MZ Li, et al. Emotion detection from EEG recordings based on supervised and unsupervised dimension reduction. Concurrency Computat Pract Exper. 2018, 30(23): e4446.
[91]
J Atkinson, D Campos. Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Syst Appl. 2016, 47: 35-41.
[92]
G Hajcak, A MacNamara, DM Olvet. Event-related potentials, emotion, and emotion regulation: an integrative review. Dev Neuropsychol. 2010, 35(2): 129-155.
[93]
EM Fraedrich, K Lakatos, G Spangler. Brain activity during emotion perception: the role of attachment representation. Attach Hum Dev. 2010, 12(3): 231-248.
[94]
N Martini, D Menicucci, L Sebastiani, et al. The dynamics of EEG gamma responses to unpleasant visual stimuli: from local activity to functional connectivity. Neuroimage. 2012, 60(2): 922-932.
[95]
L Bozhkov, P Georgieva, I Santos, et al. EEG-based subject independent affective computing models. Procedia Comput Sci. 2015, 53: 375-382.
[96]
B Hjorth. EEG analysis based on time domain properties. Electroencephalogr Clin Neurophysiol. 1970, 29(3): 306-310.
[97]
JM Hausdorff, A Lertratanakul, ME Cudkowicz, et al. Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. J Appl Physiol. 2000, 88(6): 2045-2053.
[98]
T Higuchi. Approach to an irregular time series on the basis of the fractal theory. Phys D: Nonlinear Phenom. 1988, 31(2): 277-283.
[99]
M Affinito, M Carrozzi, A Accardo, et al. Use of the fractal dimension for the analysis of electroencephalographic time series. Biol Cybern. 1997, 77(5): 339-350.
[100]
AH Kemp, RB Silberstein, SM Armstrong, et al. Gender differences in the cortical electrophysiological processing of visual emotional stimuli. Neuroimage. 2004, 21(2): 632-646.
[101]
RN Duan, JY Zhu, BL Lu. Differential entropy feature for EEG-based emotion classification. In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). San Diego, CA, USA, 2013, pp 81-84.
[102]
LC Shi, YY Jiao, BL Lu. Differential entropy feature for EEG-based vigilance estimation. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 2013, pp 1-28.
[103]
H Candra, M Yuwono, R Chai, et al. Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan, Italy, 2015, pp 7250-7253.
[104]
A Subasi. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl. 2007, 32(4): 1084-1093.
[105]
Z Mohammadi, J Frounchi, M Amiri. Wavelet- based emotion recognition system using EEG signal. Neural Comput & Applic. 2017, 28(8): 1985-1990.
[106]
I Winkler, M Jäger, V Mihajlović, et al. Frontal EEG Asymmetry based classification of emotional valence using common spatial patterns. World Academy of Science, Engineering and Technology. 2010, 70: 373-378.
[107]
M Balconi, C Lucchiari. Consciousness and arousal effects on emotional face processing as revealed by brain oscillations. A gamma band analysis. Int J Psychophysiol. 2008, 67(1): 41-46.
[108]
AM Bastos, JM Schoffelen. A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front Syst Neurosci. 2015, 9: 175.
[109]
X Wu, WL Zheng, BL Lu. Investigating EEG-based functional connectivity patterns for multimodal emotion recognition. arXiv preprint. 2020: arXiv: 2004.01973.
[110]
M Chen, J Han, L Guo, et al. Identifying valence and arousal levels via connectivity between EEG channels. In 2015 International Conference on Affective Computing and Intelligent Interaction (ACII). Xi'an, China, 2015, pp 63-69.
[111]
SE Moon, CJ Chen, CJ Hsieh, et al. Emotional EEG classification using connectivity features and convolutional neural networks. Neural Networks. 2020, 132: 96-107.
[112]
XC Liu, T Li, C Tang, et al. Emotion recognition and dynamic functional connectivity analysis based on EEG. IEEE Access. 2019, 7: 143293-143302.
[113]
R Jenke, A Peer, M Buss. Feature extraction and selection for emotion recognition from EEG. IEEE Trans Affective Comput. 2014, 5(3): 327-339.
[114]
B Nakisa, MN Rastgoo, D Tjondronegoro, et al. Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors. Expert Syst Appl. 2018, 93: 143-155.
[115]
M Soleymani, M Pantic, T Pun. Multimodal emotion recognition in response to videos. IEEE Trans Affective Comput. 2012, 3(2): 211-223.
[116]
S Hwang, K Hong, G Son, et al. Learning CNN features from DE features for EEG-based emotion recognition. Pattern Anal Applic. 2020, 23(3): 1323-1335.
[117]
S Jang, SE Moon, JS Lee. EEG-based video identification using graph signal modeling and graph convolutional neural network. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, 2018, pp 3066-3070.
[118]
TF Song, WM Zheng, P Song, et al. EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans Affective Comput. 2020, 11(3): 532-541.
[119]
HY Xu, KN Plataniotis. Affective states classification using EEG and semi-supervised deep learning approaches. In 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP), Montreal, QC, Canada, 2016, pp 1-6.
[120]
XW Li, QL Zhao, L Liu, et al. Improve affective learning with EEG approach. Comput Inform. 2010, 29: 557-570.
[121]
DF Specht. Probabilistic neural networks. Neural Networks. 1990, 3(1): 109-118.
[122]
J Zhang, M Chen, S Hu, et al. PNN for EEG-based emotion recognition. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary, 2016, pp 2-30.
[123]
A Subasi, M Ismail Gursoy. EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl. 2010, 37(12): 8659-8666.
[124]
M Li, HP Xu, XW Liu, et al. Emotion recognition from multichannel EEG signals using K-nearest neighbor classification. Technol Heal Care. 2018, 26: 509-519.
[125]
JF Jiang, Y Zeng, ZM Lin, et al. Review on EEG-based emotion assessment (in Chinese). J Inform Eng Univ, 2016, 17: 686-693.
[126]
P Bashivan, I Rish, M Yeasin, et al. Learning representations from EEG with deep recurrent- convolutional neural networks. arXiv preprint. 2015: arXiv:1511.06448.
[127]
FP Such, S Sah, MA Dominguez, et al. Robust spatial filtering with graph convolutional neural networks. IEEE J Sel Top Signal Process. 2017, 11(6): 884-896.
[128]
ZM Wang, Y Tong, X Heng. Phase-locking value based graph convolutional neural networks for emotion recognition. IEEE Access. 2019, 7: 93711-93722.
[129]
XH Wang, T Zhang, XM Xu, et al. EEG emotion recognition using dynamical graph convolutional neural networks and broad learning system. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Madrid, Spain, 2018, pp 1240-1244.
[130]
DF Wulsin, JR Gupta, R Mani, et al. Modeling electroencephalography waveforms with semi- supervised deep belief nets: fast classification and anomaly measurement. J Neural Eng. 2011, 8(3): 036015.
[131]
XW Jia, K Li, XY Li, et al. A novel semi- supervised deep learning framework for affective state recognition on EEG signals. In 2014 IEEE International Conference on Bioinformatics and Bioengineering, Boca Raton, FL, USA, 2014, pp 30-37.
Publication history
Copyright
Rights and permissions
Publication history
Received: 18 August 2020
Revised: 06 September 2020
Accepted: 22 September 2020
Published:
04 February 2021
Issue date: September 2020
Copyright
© The authors 2020
Rights and permissions
This article is published with open access at journals.sagepub.com/home/BSA
Creative Commons Non Commercial CC BY- NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/ en-us/nam/open-access-at-sage).
FEEDBACK 
TOP