Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (S1): 323-332.DOI: 10.11772/j.issn.1001-9081.2022091341
• Frontier and comprehensive applications • Previous Articles
Xudong CHEN1,2, Heng ZHONG1,2, Jie HUANGFU2, Gaochong LYU2, Cheng WANG2, Deliang WANG2(), Kai TONG1
Received:
2022-09-19
Revised:
2023-02-25
Accepted:
2022-03-02
Online:
2023-07-04
Published:
2023-06-30
Contact:
Deliang WANG
陈旭东1,2, 钟恒1,2, 皇甫洁2, 吕高冲2, 王成2, 王德良2(), 童凯1
通讯作者:
王德良
作者简介:
陈旭东(1998—),男,四川内江人,硕士研究生,主要研究方向:智能信息处理基金资助:
CLC Number:
Xudong CHEN, Heng ZHONG, Jie HUANGFU, Gaochong LYU, Cheng WANG, Deliang WANG, Kai TONG. Review of emotion recognition of EEG signals[J]. Journal of Computer Applications, 2023, 43(S1): 323-332.
陈旭东, 钟恒, 皇甫洁, 吕高冲, 王成, 王德良, 童凯. 脑电信号情绪识别综述[J]. 《计算机应用》唯一官方网站, 2023, 43(S1): 323-332.
Add to citation manager EndNote|Ris|BibTeX
URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022091341
部位 | 名称 | 代号 |
---|---|---|
前额 | Frontal Pole | Fp1、Fp2 |
额 | Frontal | F3、F4、Fz |
中央 | Central | C3、C4、Cz |
顶 | Parietal | P3、P4、Pz |
枕 | Occipital | O1、O2 |
侧额 | Inferior Frontal | F7、F8 |
颞 | Temporal | T3、T4 |
后颞 | Posterior Temporal | T5、T6 |
耳 | Auricular | A1、A2 |
部位 | 名称 | 代号 |
---|---|---|
前额 | Frontal Pole | Fp1、Fp2 |
额 | Frontal | F3、F4、Fz |
中央 | Central | C3、C4、Cz |
顶 | Parietal | P3、P4、Pz |
枕 | Occipital | O1、O2 |
侧额 | Inferior Frontal | F7、F8 |
颞 | Temporal | T3、T4 |
后颞 | Posterior Temporal | T5、T6 |
耳 | Auricular | A1、A2 |
数据集 | 采集通道数 | 情绪诱发方式 | 样本个数 | 情绪状态 |
---|---|---|---|---|
DEAP | 32 | 音乐视频 | 32 | 效价、唤醒度、优势度、喜欢度 |
eNTERFACE’06 | 16 | 从国际情感图像系统中选取 | 42 | 快乐、悲伤、惊讶、愤怒、厌恶和恐惧 |
HeadIT | — | 回忆情绪 | — | 正、负 |
SEED | 62 | 电影片段 | 15 | 中立、积极和消极 |
SEED-Ⅳ | 62 | 电影片段 | 15 | 高兴、悲伤、恐惧、中性 |
Mahnob-HCItagging | 32 | 电影和图片的片段 | 30 | 自我评估模型评估效价和唤醒度 |
MPED | 32 | 视频 | 23 | 快乐、有趣、厌恶、愤怒、恐惧、悲伤和中立 |
DREAMER | 14 | 电影片段 | 23 | 自我评估激发情感激发 |
数据集 | 采集通道数 | 情绪诱发方式 | 样本个数 | 情绪状态 |
---|---|---|---|---|
DEAP | 32 | 音乐视频 | 32 | 效价、唤醒度、优势度、喜欢度 |
eNTERFACE’06 | 16 | 从国际情感图像系统中选取 | 42 | 快乐、悲伤、惊讶、愤怒、厌恶和恐惧 |
HeadIT | — | 回忆情绪 | — | 正、负 |
SEED | 62 | 电影片段 | 15 | 中立、积极和消极 |
SEED-Ⅳ | 62 | 电影片段 | 15 | 高兴、悲伤、恐惧、中性 |
Mahnob-HCItagging | 32 | 电影和图片的片段 | 30 | 自我评估模型评估效价和唤醒度 |
MPED | 32 | 视频 | 23 | 快乐、有趣、厌恶、愤怒、恐惧、悲伤和中立 |
DREAMER | 14 | 电影片段 | 23 | 自我评估激发情感激发 |
样本 | 人数 | 通道数 | 分类方法 | 情绪维度 | 准确率 |
---|---|---|---|---|---|
DEAP[ | 32 | 32 | DCCA-GWO | 效价、唤醒度 | 效价:89.78%;唤醒度:78.5% |
SEED[ | 15 | 62 | ARF | 中立、积极、消极 | 积极:96.3%;消极:94.8%;中立:91.9% |
DREAMER[ | 23 | 14 | RACNN | 效价、唤醒度 | 效价:95.55%;唤醒度:97.01% |
音频[ | 20 | 24 | MC-LS-SVM | 快乐、恐惧、悲伤、放松 | 快乐:92.79%;恐惧:87.62%;悲伤:88.98%;放松:93.13% |
音频+视频[92,86] | 20 | 24 | DT、KNN、SVM和极限学习机 | 快乐、恐惧、悲伤、放松 | DT:87.7%;KNN:93.8%;SVM:93.1%;极限学习机:97.24%;整体准确性:97.24% |
17 | 5 | LSTM | 低唤醒正、高唤醒正、低唤醒负、高唤醒负 | 早期融合:71.61%;晚期融合:70.17% |
样本 | 人数 | 通道数 | 分类方法 | 情绪维度 | 准确率 |
---|---|---|---|---|---|
DEAP[ | 32 | 32 | DCCA-GWO | 效价、唤醒度 | 效价:89.78%;唤醒度:78.5% |
SEED[ | 15 | 62 | ARF | 中立、积极、消极 | 积极:96.3%;消极:94.8%;中立:91.9% |
DREAMER[ | 23 | 14 | RACNN | 效价、唤醒度 | 效价:95.55%;唤醒度:97.01% |
音频[ | 20 | 24 | MC-LS-SVM | 快乐、恐惧、悲伤、放松 | 快乐:92.79%;恐惧:87.62%;悲伤:88.98%;放松:93.13% |
音频+视频[92,86] | 20 | 24 | DT、KNN、SVM和极限学习机 | 快乐、恐惧、悲伤、放松 | DT:87.7%;KNN:93.8%;SVM:93.1%;极限学习机:97.24%;整体准确性:97.24% |
17 | 5 | LSTM | 低唤醒正、高唤醒正、低唤醒负、高唤醒负 | 早期融合:71.61%;晚期融合:70.17% |
1 | RAMIREZ P M, DESANTIS D, OPLER L A. EEG biofeedback treatment of ADD: a viable alternative to traditional medical intervention? [J]. Annals of the New York Academy of Sciences, 2001, 931(1): 342-358. |
2 | HU X, CHEN J, WANG F, et al. Ten challenges for EEG-based affective computing[J]. Brain Science Advances, 2019, 5(1): 1-20. 10.26599/bsa.2019.9050005 |
3 | FÜRBASS F, KURAL M A, GRITSCH G, et al. An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard[J]. Clinical Neurophysiology, 2020, 131(6): 1174-1179. 10.1016/j.clinph.2020.02.032 |
4 | HEALEY J A, PICARD R W. Detecting stress during real-world driving tasks using physiological sensors[J]. IEEE Transactions on Intelligent Transportation Systems, 2005(6):156-166. 10.1109/tits.2005.848368 |
5 | MORENCY L P, WHITEHILL J, MOVELLAN J. Generalized adaptive view-based appearance model: Integrated framework for monocular head pose estimation [C]// Proceedings of the 8th IEEE International Conference on Automatic Face & Gesture Recognition. Piscataway: IEEE, 2008: 1-8. 10.1109/afgr.2008.4813429 |
6 | SHU L, XIE J, YANG M, et al. A review of emotion recognition using physiological signals[J]. Sensors, 2018, 18(7): No.2074. 10.3390/s18072074 |
7 | CRAIK A, HE Y, CONTRERAS-VIDAL J L. Deep learning for electroencephalogram (EEG) classification tasks: a review[J]. Journal of Neural Engineering, 2019, 16(3): No.031001. 10.1088/1741-2552/ab0ab5 |
8 | KWON Y H, SHIN S B, KIM S D. Electroencephalography based fusion two-dimensional (2D)-convolution neural networks (CNN) model for emotion recognition system[J]. Sensors, 2018, 18(5): No.1383. 10.3390/s18051383 |
9 | MAHESHWARI D, GHOSH S K, TRIPATHY R K, et al. Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals[J]. Computers in Biology and Medicine, 2021, 134: No.104428. 10.1016/j.compbiomed.2021.104428 |
10 | KEELAWAT P, THAMMASAN N, NUMAO M, et al. A comparative study of window size and channel arrangement on EEG-emotion recognition using deep CNN[J]. Sensors, 2021, 21(5): No.1678. 10.3390/s21051678 |
11 | LI Z, TIAN X, SHU L, et al. Emotion recognition from EEG using RASM and LSTM [C]// Proceedings of the 2017 International Conference on Internet Multimedia Computing and Service. Cham: Springer, 2017: 310-318. 10.1007/978-981-10-8530-7_30 |
12 | LIU J, SU Y, LIU Y. Multi-modal emotion recognition with temporal-band attention based on LSTM-RNN[C]// Proceedings of the 18th Advances in Multimedia Information Processing—Pacific-Rim Conference on Multimedia. Cham: Springer, 2018: 194-204. 10.1007/978-3-319-77380-3_19 |
13 | SHARMA R, PACHORI R B, SIRCAR P. Automated emotion recognition based on higher order statistics and deep learning algorithm[J]. Biomedical Signal Processing and Control, 2020, 58: No.101867. 10.1016/j.bspc.2020.101867 |
14 | LUO Y, LU B. EEG data augmentation for emotion recognition using a conditional Wasserstein GAN[C]// Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine & Biology Society. Piscataway: IEEE,2018:2535-2538. 10.1109/embc.2018.8512865 |
15 | 拉杰什 P N 拉奥. 脑机接口导论[M]. 张莉,陈民铀,译. 北京:机械工业出版社, 2016: 5-6. |
16 | HAMMOND CORYDON D. What is neurofeedback: an update[J]. Journal of Neurotherapy, 2011, 15(4):305-336. 10.1080/10874208.2011.623090 |
17 | 魏景汉, 罗跃嘉. 事件相关电位原理与技术[M]. 北京: 科学出版社, 2010:1-2. |
18 | 齐建林, 董燕, 苗丹民, 等. 恢复性睡眠对43h完全睡眠剥夺后任务管理功能影响[J]. 中国民族民间医药, 2009, 18(12):21-22. 10.3969/j.issn.1007-8517.2009.12.012 |
19 | 王忠民,赵玉鹏,郑镕林 等.脑电信号情绪识别研究综述[J].计算机科学与探索,2022,16(4):760-774. 10.3778/j.issn.1673-9418.2107006 |
20 | KLEM G H, LÜDERS H O, JASPER H H, et al. The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology[J]. Electroencephalography and Clinical Neurophysiology, 1999, 52: 3-6. |
21 | 罗新勇. 基于流形学习的脑电特征提取方法及应用[D]. 北京:北京工业大学, 2016: 5-6. |
22 | PEDRONI A, BAHREINI A, LANGER N. Automagic: Standardized preprocessing of big EEG data[J]. NeuroImage, 2019, 200: 460-473. 10.1016/j.neuroimage.2019.06.046 |
23 | DELORME A, MAKEIG S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis[J]. Journal of Neuroscience Methods, 2004, 134(1): 9-21. 10.1016/j.jneumeth.2003.10.009 |
24 | KOELSTRA S, MUHL C, SOLEYMANI M, et al. DEAP: a data base for emotion analysis using physiological signals[J]. IEEE Transactions on Affective Computing, 2011, 3(1):18-31. 10.1109/t-affc.2011.15 |
25 | SAVRAN A, CIFTCI K, CHANEL G, et al. Emotion detection in the loop from brain signals and facial images [EB/OL]. [2022-06-13]. . |
26 | Swartz Center for Computational Neuroscience. HeadIT : Human Electrophysiology, Anatomic Data and Integrated Tools Resource [EB/OL]. [2022-07-12]. . |
27 | ZHENG W, LU B. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks[J]. IEEE Transactions on Autonomous Mental Development, 2015, 7(3):162-175. 10.1109/tamd.2015.2431497 |
28 | ZHENG W, LIU W, LU Y, et al. EmotionMeter: a multimodal frame work for recognizing human emotions[J]. IEEE Transactions on Cybernetics, 2019, 49(3):1110-1122. 10.1109/tcyb.2018.2797176 |
29 | SOLEYMANI M, LICHTENAUER J, PUN T, et al. A multimodal database for affect recognition and implicit tagging[J]. IEEE Transactions on Affective Computing, 2011, 3(1): 42-55. 10.1109/t-affc.2011.25 |
30 | SONG T, ZHENG W, LU C, et al. MPED: A multi-modal physiological emotion database for discrete emotion recognition[J]. IEEE Access, 2019, 7: 12177-12191. 10.1109/access.2019.2891579 |
31 | KATSIGIANNIS S, RAMZAN N. DREAMER: a database for emotion recognition through EEG and ECG signals from wireless lowcost off-the-shelf devices[J]. IEEE Journal of Biomedical and Health Informatics, 2018, 22(1):98-107. 10.1109/jbhi.2017.2688239 |
32 | PANE E S, WIBAWA A D, PURNOMO M H. Improving the accuracy of EEG emotion recognition by combining valence lateralization and ensemble learning with tuning parameters[J]. Cognitive Processing, 2019, 20: 405-417. 10.1007/s10339-019-00924-z |
33 | CHENG J, CHEN M, LI C, et al. Emotion recognition from multi-channel eeg via deep forest[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 25(2): 453-464. 10.1109/jbhi.2020.2995767 |
34 | YIN Y, ZHENG X, HU B, et al. EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM[J]. Applied Soft Computing, 2021, 100: No.106954. 10.1016/j.asoc.2020.106954 |
35 | HUANG D, CHEN S, LIU C, et al. Differences first in asymmetric brain: A bi-hemisphere discrepancy convolutional neural network for EEG emotion recognition[J]. Neurocomputing, 2021, 448: 140-151. 10.1016/j.neucom.2021.03.105 |
36 | MOKATREN L S, ANSARI R, CETIN A E, et al. EEG classification by factoring in sensor spatial configuration[J]. IEEE Access, 2021, 9: 19053-19065. 10.1109/access.2021.3054670 |
37 | MOON S E, CHEN C J, HSIEH C J, et al. Emotional EEG classification using connectivity features and convolutional neural networks[J]. Neural Networks, 2020, 132: 96-107. 10.1016/j.neunet.2020.08.009 |
38 | LIU J, WU G, LUO Y, et al. EEG-based emotion classification using a deep neural network and sparse autoencoder[J]. Frontiers in Systems Neuroscience, 2020, 14: No.43. 10.3389/fnsys.2020.00043 |
39 | CHEN J, JIANG D, ZHANG Y, et al. Emotion recognition from spatiotemporal EEG representations with hybrid convolutional recurrent neural networks via wearable multi-channel headset[J]. Composites Communications, 154, 58-65. 10.1016/j.comcom.2020.02.051 |
40 | YIN Z, LIU L, CHEN J, et al. Locally robust EEG feature selection for individual-independent emotion recognition[J]. Expert Systems with Applications, 2020, 162: No.113768. 10.1016/j.eswa.2020.113768 |
41 | ZHONG M, YANG Q, LIU Y, et al. EEG emotion recognition based on TQWT-features and hybrid convolutional recurrent neural network[J]. Biomedical Signal Processing and Control, 2023, 79: No.104211. 10.1016/j.bspc.2022.104211 |
42 | SUBASI A, TUNCER T, DOGAN S, et al. EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier[J]. Biomedical Signal Processing and Control, 2021, 68: No.102648. 10.1016/j.bspc.2021.102648 |
43 | WEI C, CHEN L, SONG Z, et al. EEG-based emotion recognition using simple recurrent units network and ensemble learning[J]. Biomedical Signal Processing and Control, 2020, 58: 101756. 10.1016/j.bspc.2019.101756 |
44 | TOPIC A, RUSSO M. Emotion recognition based on EEG feature maps through deep learning network[J]. Engineering Science and Technology, an International Journal, 2021, 24(6): 1442-1454. 10.1016/j.jestch.2021.03.012 |
45 | WANG Z, TONG Y, HENG X. Phase-locking value based graph convolutional neural networks for emotion recognition[J]. IEEE Access, 2019, 7: 93711-93722. 10.1109/access.2019.2927768 |
46 | LI Y, FU B, LI F, et al. A novel transferability attention neural network model for EEG emotion recognition[J]. Neurocomputing, 2021, 447: 92-101. 10.1016/j.neucom.2021.02.048 |
47 | WANG F, WU S, ZHANG W, et al. Emotion recognition with convolutional neural network and EEG-based EFDMs[J]. Neuropsychologia, 2020, 146: No.107506. 10.1016/j.neuropsychologia.2020.107506 |
48 | ASGHAR M A, KHAN M J, RIZWAN M, et al. AI inspired EEG-based spatial feature selection method using multivariate empirical mode decomposition for emotion classification[J]. Multimedia Systems, 2022, 28(4): 1275-1288. 10.1007/s00530-021-00782-w |
49 | LU Y, WANG M, WU W, et al. Dynamic entropy-based pattern learning to identify emotions from EEG signals across individuals[J]. Measurement, 2020, 150: No.107003. 10.1016/j.measurement.2019.107003 |
50 | RAHMAN M A, HOSSAIN M F, HOSSAIN M, et al. Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal[J]. Egyptian Informatics Journal, 2020, 21(1): 23-35. 10.1016/j.eij.2019.10.002 |
51 | RIEDL M, MÜLLER A, WESSEL N. Practical considerations of permutation entropy: A tutorial review[J]. The European Physical Journal Special Topics, 2013, 222(2): 249-262. 10.1140/epjst/e2013-01862-7 |
52 | NAMAZI H, AGHASIAN E, ALA T S. Complexity-based classification of EEG signal in normal subjects and patients with epilepsy[J]. Technology and Health Care, 2020, 28(1):57-66. 10.3233/thc-181579 |
53 | 周广东,丁幼亮,李爱群,等.基于小波变换的非平稳脉动风时变功率谱估计方法研究[J].工程力学,2013,30(3):89-97. |
54 | YI X, QU A H. Matlab simulation analysis of power spectrum estimation based on Welch method[J]. Modern Electronics Technique, 2010, 33: 7-9. |
55 | O’TOOLE J. Discrete quadratic time-frequency distributions: definition, computation, and a newborn electroencephalogram application [EB/OL]. [2022-04-03]. . 10.1109/tsp.2009.2031287 |
56 | ALAZRAI R, HOMOUD R, ALWANNI H, et al. EEG-based emotion recognition using quadratic time-frequency distribution[J]. Sensors, 2018, 18(8): 2739. 10.3390/s18082739 |
57 | KOENIG W, DUNN H K, LACY L Y. The sound spectrograph[J]. The Journal of the Acoustical Society of America, 1946, 18(1): 19-49. 10.1121/1.1916342 |
58 | GAO Q, WANG C, WANG Z, et al. EEG based emotion recognition using fusion feature extraction method[J]. Multimedia Tools and Applications, 2020, 79: 27057-27074. 10.1007/s11042-020-09354-y |
59 | ROSSO O A, BLANCO S, YORDANOVA J, et al. Wavelet entropy: a new tool for analysis of short duration brain electrical signals[J]. Journal of Neuroscience Methods, 2001, 105(1): 65-75. 10.1016/s0165-0270(00)00356-3 |
60 | BASTOS-FILHO T F, FERREIRA A, ATENCIO A C, et al. Evaluation of feature extraction techniques in emotional state recognition[C]// Proceedings of the 4th International Conference on Intelligent Human Computer Interaction. Piscataway: IEEE, 2012: 1-6. 10.1109/ihci.2012.6481860 |
61 | LI M, XU H, LIU X, et al. Emotion recognition from multichannel EEG signals using K-nearest neighbor classification[J]. Technology and Health Care, 2018, 26(S1): 509-519. 10.3233/thc-174836 |
62 | HATAMIKIA S, MAGHOOLI K, NASRABADI A M. The emotion recognition system based on autoregressive model and sequential forward feature selection of electroencephalogram signals[J]. Journal of Medical Signals and Sensors, 2014, 4(3): No.194. 10.4103/2228-7477.137777 |
63 | GUO K, YU H, CHAI R, et al. A hybrid physiological approach of emotional reaction detection using combined FCM and SVM classifier [C]// Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway: IEEE, 2019: 7088-7091. 10.1109/embc.2019.8857698 |
64 | ATKINSON J, CAMPOS D. Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers[J]. Expert Systems with Applications, 2016, 47: 35-41. 10.1016/j.eswa.2015.10.049 |
65 | ALSOLAMY M, FATTOUH A. Emotion estimation from EEG signals during listening to Quran using PSD features[C]// Proceedings of the 7th International Conference on Computer Science and Information Technology. Piscataway: IEEE, 2016: 1-5. 10.1109/csit.2016.7549457 |
66 | WANG X, NIE D, LU B L. Emotional state classification from EEG data using machine learning approach[J], Neurocomputing, 2014, 129: 94-106. 10.1016/j.neucom.2013.06.046 |
67 | JALILIFARD A, PIZZOLATO E B, ISLAM M K, Emotion classification using single-channel scalp-EEG recording [C]// Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway: IEEE, 2016: 845-849. 10.1109/embc.2016.7590833 |
68 | LIU Y, WU C, KAO Y, et al. Single-trial EEG-based emotion recognition using kernel Eigen-emotion pattern and adaptive support vector machine [C]// Proceedings of the 35th Annual International Conference of the IEEE Engineeringin Medicine and Biology Society. Piscataway: IEEE, 2013: 4306-4309. 10.1109/embc.2013.6610498 |
69 | SHAHABI H, MOGHIMI S. Toward automatic detection of brain responses to emotional music through analysis of EEG effective connectivity[J]. Computers in Human Behavior, 2016, 58: 231-239. 10.1016/j.chb.2016.01.005 |
70 | HUANG D, GUAN C, ANG K K, et al. Asymmetric spatial pattern for EEG-based emotion detection[C]// Proceedings of the 2012 International Joint Conference on Neural Networks. Piscataway: IEEE, 2012: 1-7. 10.1109/ijcnn.2012.6252390 |
71 | BHATTI A M, MAJID M, ANWAR S M, et al. Human emotion recognition and analysis in response to audio music using brain signals[J]. Computers in Human Behavior, 2016, 65: 267-275. 10.1016/j.chb.2016.08.029 |
72 | ZHENG W L, ZHU J Y, LU B L. Identifying stable patterns over time for emotion recognition from EEG[J]. IEEE Transactions on Affective Computing, 2019, 10(3): 417-429. 10.1109/taffc.2017.2712143 |
73 | ZHENG W L, SANTANA R, LU B L. Comparison of classification methods for EEG-based emotion recognition[C]// Proceedings of the 2015 World Congress on Medical Physics and Biomedical Engineering. Cham: Springer, 2015: 1184-1187. 10.1007/978-3-319-19387-8_287 |
74 | LIU N, FANG Y, LI L, et al. Multiple feature fusion for automatic emotion recognition using EEG signals [C]// Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2018: 896-900. 10.1109/icassp.2018.8462518 |
75 | MASOOD N, FAROOQ H. Investigating EEG patterns for dual-stimuli induced human fear emotional state[J]. Sensors, 2019, 19(3): No.522. 10.3390/s19030522 |
76 | HADJIDIMITRIOU S K, HADJILEONTIADIS L J. Toward an EEG-based recognition of music liking using time-frequency analysis[J]. IEEE Transactions on Biomedical Engineering, 2012, 59(12): 3498-3510. 10.1109/tbme.2012.2217495 |
77 | ACKERMANN P, KOHLSCHEIN C, BITSCH J A, et al. EEG-based automatic emotion recognition: feature extraction, selection and classification methods [C]// Proceedings of the 18th International Conference on E-Health Networking, Applications and Services. Piscataway: IEEE, 2016: 1-6. 10.1109/healthcom.2016.7749447 |
78 | BHATTACHARYYA A, TRIPATHY R K, GARG L, et al. A novel multivariate-multiscale approach for computing EEG spectral and temporal complexity for human emotion recognition[J]. IEEE Sensors Journal, 2020, 21(3): 3579-3591. 10.1109/jsen.2020.3027181 |
79 | QING C, QIAO R, XU X, et al. Interpretable emotion recognition using EEG signals[J]. IEEE Access, 2019, 7: 94160-94170. 10.1109/access.2019.2928691 |
80 | GONZALEZ H A, YOO J, ELFADEL I M. EEG-based emotion detection using unsupervised transfer learning [C]// Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway: IEEE, 2019: 694-697. 10.1109/embc.2019.8857248 |
81 | WANG Y, QIU S, ZHAO C, et al. EEG-based emotion recognition with prototype-based data representation [C]// Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway: IEEE, 2019: 684-689. 10.1109/embc.2019.8857340 |
82 | SOHAIB A T, QURESHI S, HAGELBÄCK J, et al. Evaluating classifiers for emotion recognition using EEG [C]// Proceedings of the 2013 Foundations of Augmented Cognition. Cham: Springer, 2013: 492-501. 10.1007/978-3-642-39454-6_53 |
83 | MERT A, AKAN A. Emotion recognition based on time-frequency distribution of EEG signals using multivariate synchrosqueezing transform[J]. Digital Signal Processing, 2018, 81: 106-115. 10.1016/j.dsp.2018.07.003 |
84 | CHAO H, DONG L, LIU Y, et al. Emotion recognition from multiband EEG signals using CapsNet[J]. Sensors, 2019, 19(9): No.2212. 10.3390/s19092212 |
85 | NAKISA B, RASTGOO M N, RAKOTONIRAINY A, et al. Automatic emotion recognition using temporal multimodal deep learning[J]. IEEE Access, 2020, 8: 225463-225474. 10.1109/access.2020.3027026 |
86 | TAO W, LI C, SONG R, et al. EEG-based emotion recognition via channel-wise attention and self attention[J]. IEEE Transactions on Affective Computing, 2023, 14(1): 382-393. 10.1109/taffc.2020.3025777 |
87 | CUI H, LIU A, ZHANG X, et al. EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network[J]. Knowledge-Based Systems, 2020, 205: No.106243. 10.1016/j.knosys.2020.106243 |
88 | LUO Y, FU Q, XIE J, et al. EEG-based emotion classification using spiking neural networks[J]. IEEE Access, 2020,8: 46007-46016. 10.1109/access.2020.2978163 |
89 | ZHANG Y, CHENG C, ZHANG Y, Multimodal emotion recognition using a hierarchical fusion convolutional neural network[J]. IEEE Access, 2021, 9: 7943-7951. 10.1109/access.2021.3049516 |
90 | SINGH U, SHAW R, PATRA B K. A data augmentation and channel selection technique for grading human emotions on DEAP dataset[J]. Biomedical Signal Processing and Control, 2023, 79: No.104060. 10.1016/j.bspc.2022.104060 |
91 | TARAN S, BAJAJ V. Emotion recognition from single-channel EEG signals using a two-stage correlation and instantaneous frequency-based filtering method[J]. Computer Methods and Programs in Biomedicine, 2019, 173: 157-165. 10.1016/j.cmpb.2019.03.015 |
92 | KHARE S K, BAJAJ V. An evolutionary optimized variational mode decomposition for emotion recognition[J]. IEEE Sensors Journal, 2020, 21(2): 2035-2042. 10.1109/jsen.2020.3020915 |
[1] | Xiwei LIU, Xiaoyan GONG, Hongxia ZHAO, Siyu BIAN, Shuai SHAO, Yaping DAI, Wenxin DAI. Dynamic facial expression recognition based on hybrid attention mechanism [J]. Journal of Computer Applications, 2023, 43(S1): 1-7. |
[2] | Kui JIANG, Zhihang YU, Xiaolei CHEN, Yuhao LI. Design and implementation of Webshell traffic detection system based on BERT-CNN [J]. Journal of Computer Applications, 2023, 43(S1): 126-132. |
[3] | Dong WANG, Xian ZHANG, Da LI, Qinglei GUO, Xin CHANG, Jingli FENG. Blockchain security protection scheme for power grid based on distributed anomaly detection [J]. Journal of Computer Applications, 2023, 43(S1): 139-146. |
[4] | Fan DENG, Yuan ZENG, Bowen LIU, Boyuan JIANG, Chongyang ZHONG, Shihong XIA. Gait recognition model based on temporal feature aggregation with Transformer [J]. Journal of Computer Applications, 2023, 43(S1): 15-18. |
[5] | Pengliu TAN, Guangyong XU, Luyu ZHANG, Runshu WANG. Heart disease prediction model based on convolutional neural network and Adaboost [J]. Journal of Computer Applications, 2023, 43(S1): 19-25. |
[6] | Jingqiao LU, Wei BIN, Yongqiang LU, Guangzhu MAI, Yin CHEN, Yanxiong WU. Fine-grained visual classification combining attention mutual exclusion regularization [J]. Journal of Computer Applications, 2023, 43(S1): 224-228. |
[7] | Yiren LI, Pei SHEN. Foreign object detection method for business demand of scrap steel recycling [J]. Journal of Computer Applications, 2023, 43(S1): 243-249. |
[8] | Quanyou SHEN, Xiaobo ZHANG, Wenhao LI, Lihan LI, Rongde XU, Daohua CHEN, Jing LI. Progress of U-Net applicaitons to lung nodule segmentation [J]. Journal of Computer Applications, 2023, 43(S1): 250-257. |
[9] | Xiajiao ZHONG, Shaobing ZHANG, Jing GUO, Shengchao WANG, Miao CHENG, Lian HE, Yimin ZHAO. 3D point cloud tooth and jaw segmentation and identification based on RandLA-Net [J]. Journal of Computer Applications, 2023, 43(S1): 269-275. |
[10] | Xuelin WANG, Lixue DU, Dejin CHEN, Xiaqing ZHANG, Tao XU, Yaxin CHEN, Zhangwei YU. Localization of automobile fuel tank cover based on deep learning and binocular vision [J]. Journal of Computer Applications, 2023, 43(S1): 281-287. |
[11] | Jing QIN, Xueqian MA, Fujie GAO, Changqing JI, Zumin WANG. Survey of Parkinson’s disease auxiliary diagnosis methods based on gait analysis [J]. Journal of Computer Applications, 2023, 43(6): 1687-1695. |
[12] | Yichi CHEN, Bin CHEN. Review of lifelong learning in computer vision [J]. Journal of Computer Applications, 2023, 43(6): 1785-1795. |
[13] | Xin JIN, Yangchuan LIU, Yechen ZHU, Zijian ZHANG, Xin GAO. Sinogram inpainting for sparse-view cone-beam computed tomography image reconstruction based on residual encoder-decoder generative adversarial network [J]. Journal of Computer Applications, 2023, 43(6): 1950-1957. |
[14] | Runting DONG, Li WU, Xiaoying WANG, Tengfei CAO, Jianqiang HUANG, Qin GUAN, Jiexia WU. Review of application analysis and research progress of deep learning in weather forecasting [J]. Journal of Computer Applications, 2023, 43(6): 1958-1968. |
[15] | Xianlan WANG, Jinkun ZHOU, Nan MU, Chen WANG. Cross-view geo-localization method based on multi-task joint learning [J]. Journal of Computer Applications, 2023, 43(5): 1625-1635. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||