Review of Affective Detection Based on Functional Magnetic Resonance Imaging

XU Shuyue; ZHOU Yongjie; LI Linling; ZHANG Li; HUANG Gan; ZHANG Zhiguo; LIANG Zhen

doi:10.11999/JEIT210534

Volume 44 Issue 2

Feb. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(2): 424-436

XU Shuyue, ZHOU Yongjie, LI Linling, ZHANG Li, HUANG Gan, ZHANG Zhiguo, LIANG Zhen. Review of Affective Detection Based on Functional Magnetic Resonance Imaging[J]. Journal of Electronics & Information Technology, 2022, 44(2): 424-436. doi: 10.11999/JEIT210534

Citation:

XU Shuyue, ZHOU Yongjie, LI Linling, ZHANG Li, HUANG Gan, ZHANG Zhiguo, LIANG Zhen. Review of Affective Detection Based on Functional Magnetic Resonance Imaging[J]. Journal of Electronics & Information Technology, 2022, 44(2): 424-436. doi: 10.11999/JEIT210534

Citation:

PDF( 618 KB)

Review of Affective Detection Based on Functional Magnetic Resonance Imaging

doi: 10.11999/JEIT210534

1.
Health Science Center School of Biomedical Engineering, Shenzhen University, Shenzhen 518071, China
2.
Recovery Treatment Department, Shenzhen Kangning Hospital, Shenzhen 518020, China

Funds: The National Natural Science Foundation of China (61906122, 91859122), Tencent “Rhinoceros Birds” - Scientific Research Foundation for Young Teachers of Shenzhen University

Received Date: 2021-06-07
Accepted Date: 2022-01-24
Rev Recd Date: 2022-01-20

Available Online: 2022-01-24

Publish Date: 2022-02-25

Abstract

Abstract

Emotion is a subjective feeling to internal or external events with positive and negative meanings, and plays an important role in human's daily life. Emotion decoding aims to automatically discriminate different emotional states by decoding physiological signals. Through estimating emotion changes, it helps to solve the practical problems in the clinical applications and develop more friendly human-interaction systems. Nowadays,functional Magnetic Resonance Imaging (fMRI)based emotion decoding is one of the most commonly used methods for deepening our understanding of the emotion-related brain dynamics and boosting the development of affective intelligence.This paper introduces current research progress, applications and main problems in the field of fMRI-based emotion decoding technique, covering experimental design, data acquisition, existing emotion-related fMRI dataset, data processing, feature extraction, and emotional pattern learning and classification.
- Emotion,
- Emotion decoding,
- Functional Magnetic Resonance Imaging(fMRI),
- Emotion feature,
- Machine learning

FullText(HTML)

References(100)

References

[1]	HOEMANN K, WU R, LOBUE V, et al. Developing an understanding of emotion categories: Lessons from objects[J]. Trends in Cognitive Sciences, 2020, 24(1): 39–51. doi: 10.1016/j.tics.2019.10.010
[2]	DOLAN R J. Emotion, cognition, and behavior[J]. Science, 2002, 298(5596): 1191–1194. doi: 10.1126/science.1076358
[3]	聂聃, 王晓韡, 段若男, 等. 基于脑电的情绪识别研究综述[J]. 中国生物医学工程学报, 2012, 31(4): 595–606. doi: 10.3969/j.issn.0258-8021.2012.04.018 NIE Dan, WANG Xiaowei, DUAN Ruonan, et al. A survey on EEG based emotion recognition[J]. Chinese Journal of Biomedical Engineering, 2012, 31(4): 595–606. doi: 10.3969/j.issn.0258-8021.2012.04.018
[4]	EBNEABBASI A, MAHDIPOUR M, NEJATI V, et al. Emotion processing and regulation in major depressive disorder: A 7T resting-state fMRI study[J]. Human Brain Mapping, 2021, 42(3): 797–810. doi: 10.1002/hbm.25263
[5]	HE Zongling, LU Fengmei, SHENG Wei, et al. Functional dysconnectivity within the emotion-regulating system is associated with affective symptoms in major depressive disorder: A resting-state fMRI study[J]. Australian & New Zealand Journal of Psychiatry, 2019, 53(6): 528–539. doi: 10.1177/0004867419832106
[6]	KRYZA-LACOMBE M, BROTMAN M A, REYNOLDS R C, et al. Neural mechanisms of face emotion processing in youths and adults with bipolar disorder[J]. Bipolar Disorders, 2019, 21(4): 309–320. doi: 10.1111/bdi.12768
[7]	ZHANG Li, LI Wenfei, WANG Long, et al. Altered functional connectivity of right inferior frontal gyrus subregions in bipolar disorder: A resting state fMRI study[J]. Journal of Affective Disorders, 2020, 272: 58–65. doi: 10.1016/j.jad.2020.03.122
[8]	HWANG H C, KIM S M, and HAN D H. Different facial recognition patterns in schizophrenia and bipolar disorder assessed using a computerized emotional perception test and fMRI[J]. Journal of Affective Disorders, 2021, 279: 83–88. doi: 10.1016/j.jad.2020.09.125
[9]	REN Fuji and BAO Yanwei. A review on human-computer interaction and intelligent robots[J]. International Journal of Information Technology & Decision Making, 2020, 19(1): 5–47. doi: 10.1142/S0219622019300052
[10]	VESISENAHO M, JUNTUNEN M, HÄKKINEN P, et al. Virtual reality in education: Focus on the role of emotions and physiological reactivity[J]. Journal of Virtual Worlds Research, 2019, 12(1): 1–15. doi: 10.4101/jvwr.v12i1.7329
[11]	SAWANGJAI P, HOMPOONSUP S, LEELAARPORN P, et al. Consumer grade EEG measuring sensors as research tools: A review[J]. IEEE Sensors Journal, 2020, 20(8): 3996–4024. doi: 10.1109/JSEN.2019.2962874
[12]	MURIAS K, SLONE E, TARIQ S, et al. Development of spatial orientation skills: An fMRI study[J]. Brain Imaging and Behavior, 2019, 13(6): 1590–1601. doi: 10.1007/s11682-018-0028-5
[13]	RICHARDSON H and SAXE R. Development of predictive responses in theory of mind brain regions[J]. Developmental Science, 2020, 23(1): e12863. doi: 10.1111/desc.12863
[14]	EICKHOFF S B, MILHAM M, and VANDERWAL T. Towards clinical applications of movie fMRI[J]. NeuroImage, 2020, 217: 116860. doi: 10.1016/j.neuroimage.2020.116860
[15]	LI Qiongge, DEL FERRARO G, PASQUINI L, et al. Core language brain network for fMRI language task used in clinical applications[J]. Network Neuroscience, 2020, 4(1): 134–154. doi: 10.1162/netn_a_00112
[16]	LAWRENCE S J D, FORMISANO E, MUCKLI L, et al. Laminar fMRI: Applications for cognitive neuroscience[J]. NeuroImage, 2019, 197: 785–791. doi: 10.1016/j.neuroimage.2017.07.004
[17]	HERWIG U, LUTZ J, SCHERPIET S, et al. Training emotion regulation through real-time fMRI neurofeedback of amygdala activity[J]. NeuroImage, 2019, 184: 687–696. doi: 10.1016/j.neuroimage.2018.09.068
[18]	WEBER-GOERICKE F and MUEHLHAN M. A quantitative meta-analysis of fMRI studies investigating emotional processing in excessive worriers: Application of activation likelihood estimation analysis[J]. Journal of Affective Disorders, 2019, 243: 348–359. doi: 10.1016/j.jad.2018.09.049
[19]	PUTKINEN V, NAZARI-FARSANI S, SEPPÄLÄ K, et al. Decoding music-evoked emotions in the auditory and motor cortex[J]. Cerebral Cortex, 2021, 31(5): 2549–2560. doi: 10.1093/cercor/bhaa373
[20]	JAMES W. II. —What is an emotion?[J]. Mind, 1884, os-IX(34): 188–205. doi: 10.1093/mind/os-IX.34.188
[21]	CANNON W B. The James-Lange theory of emotions: A critical examination and an alternative theory[J]. The American Journal of Psychology, 1927, 39(1/4): 106–124. doi: 10.2307/1415404
[22]	HEALEY J A. Wearable and automotive systems for affect recognition from physiology[D]. [Ph. D. dissertation], Massachusetts Institute of Technology, 2000.
[23]	EKMAN P and CORDARO D. What is meant by calling emotions basic[J]. Emotion Review, 2011, 3(4): 364–370. doi: 10.1177/1754073911410740
[24]	VAN DEN BROEK E L. Ubiquitous emotion-aware computing[J]. Personal and Ubiquitous Computing, 2013, 17(1): 53–67. doi: 10.1007/s00779-011-0479-9
[25]	CABANAC M. What is emotion?[J]. Behavioural Processes, 2002, 60(2): 69–83. doi: 10.1016/s0376-6357(02)00078-5
[26]	KRAGEL P A and LABAR K S. Decoding the nature of emotion in the brain[J]. Trends in Cognitive Sciences, 2016, 20(6): 444–455. doi: 10.1016/j.tics.2016.03.011
[27]	JASTORFF J, HUANG Yun’an, GIESE M A, et al. Common neural correlates of emotion perception in humans[J]. Human Brain Mapping, 2015, 36(10): 4184–4201. doi: 10.1002/hbm.22910
[28]	KASSAM K S, MARKEY A R, CHERKASSKY V L, et al. Identifying emotions on the basis of neural activation[J]. PLoS One, 2013, 8(6): e66032. doi: 10.1371/journal.pone.0066032
[29]	RUBIN D C and TALARICO J M. A comparison of dimensional models of emotion: Evidence from emotions, prototypical events, autobiographical memories, and words[J]. Memory, 2009, 17(8): 802–808. doi: 10.1080/09658210903130764
[30]	HANJALIC A and XU Liqun. Affective video content representation and modeling[J]. IEEE Transactions on Multimedia, 2005, 7(1): 143–154. doi: 10.1109/TMM.2004.840618
[31]	SUN Kai, YU Junqing, HUANG Yue, et al. An improved valence-arousal emotion space for video affective content representation and recognition[C]. 2009 IEEE International Conference on Multimedia and Expo, New York, USA, 2009: 566–569.
[32]	BUECHEL S and HAHN U. Emotion analysis as a regression problem-dimensional models and their implications on emotion representation and metrical evaluation[C]. 22nd European Conference on Artificial Intelligence, Hague, Netherlands, 2016: 1114–1122.
[33]	MALHI G S, DAS P, OUTHRED T, et al. Cognitive and emotional impairments underpinning suicidal activity in patients with mood disorders: An fMRI study[J]. Acta Psychiatrica Scandinavica, 2019, 139(5): 454–463. doi: 10.1111/acps.13022
[34]	SCHMIDT S N L, SOJER C A, HASS J, et al. fMRI adaptation reveals: The human mirror neuron system discriminates emotional valence[J]. Cortex, 2020, 128: 270–280. doi: 10.1016/j.cortex.2020.03.026
[35]	WEST H V, BURGESS G C, DUST J, et al. Amygdala activation in cognitive task fMRI varies with individual differences in cognitive traits[J]. Cognitive, Affective,& Behavioral Neuroscience, 2021, 21(1): 254–264. doi: 10.3758/s13415-021-00863-3
[36]	LI Jian, ZHONG Yuan, MA Zijuan, et al. Emotion reactivity-related brain network analysis in generalized anxiety disorder: A task fMRI study[J]. BMC Psychiatry, 2020, 20(1): 429. doi: 10.1186/s12888-020-02831-6
[37]	IVES-DELIPERI V L, SOLMS M, and MEINTJES E M. The neural substrates of mindfulness: An fMRI investigation[J]. Social Neuroscience, 2011, 6(3): 231–242. doi: 10.1080/17470919.2010.513495
[38]	PAULING L and CORYELL C D. The magnetic properties and structure of hemoglobin, oxyhemoglobin and carbonmonoxyhemoglobin[J]. Proceedings of the National Academy of Sciences of the United States of America, 1936, 22(4): 210–216. doi: 10.1073/pnas.22.4.210
[39]	OGAWA S, LEE T M, KAY A R, et al. Brain magnetic resonance imaging with contrast dependent on blood oxygenation[J]. Proceedings of the National Academy of Sciences of the United States of America, 1990, 87(24): 9868–9872. doi: 10.1073/pnas.87.24.9868
[40]	CHEN Shengyong and LI Xiaoli. Functional magnetic resonance imaging for imaging neural activity in the human brain: The annual progress[J]. Computational and Mathematical Methods in Medicine, 2012, 2012: 613465. doi: 10.1155/2012/613465
[41]	FRISTON K J, JEZZARD P, and TURNER R. Analysis of functional MRI time-series[J]. Human Brain Mapping, 1994, 1(2): 153–171. doi: 10.1002/hbm.460010207
[42]	VALENTE G, KAAS A L, FORMISANO E, et al. Optimizing fMRI experimental design for MVPA-based BCI control: Combining the strengths of block and event-related designs[J]. NeuroImage, 2019, 186: 369–381. doi: 10.1016/j.neuroimage.2018.10.080
[43]	PHAN K L, TAYLOR S F, WELSH R C, et al. Activation of the medial prefrontal cortex and extended amygdala by individual ratings of emotional arousal: A fMRI study[J]. Biological Psychiatry, 2003, 53(3): 211–215. doi: 10.1016/s0006-3223(02)01485-3
[44]	KREIFELTS B, ETHOFER T, GRODD W, et al. Audiovisual integration of emotional signals in voice and face: An event-related fMRI study[J]. NeuroImage, 2007, 37(4): 1445–1456. doi: 10.1016/j.neuroimage.2007.06.020
[45]	LAURENT H, FINNEGAN M K, and HAIGLER K. Postnatal affective MRI dataset[EB/OL]. https://doi.org/10.18112/openneuro.ds003136.v1.0.0, 2020.
[46]	BABAYAN A, BACZKOWSKI B, COZATL R, et al. MPI-Leipzig_Mind-brain-body[EB/OL]. https://doi.org/10.18112/openneuro.ds000221.v1.0.0, 2020.
[47]	VAN ESSEN D C, SMITH S M, BARCH D M, et al. The WU-Minn human connectome project: An overview[J]. NeuroImage, 2013, 80: 62–79. doi: 10.1016/j.neuroimage.2013.05.041
[48]	FINNEGAN M K, KANE S, HELLER W, et al. Mothers’ neural response to valenced infant interactions predicts postpartum depression and anxiety[J]. PLoS One, 2021, 16(4): e0250487. doi: 10.1371/journal.pone.0250487
[49]	DAVID I and BARRIOS F. Localizing brain function based on full multivariate activity patterns: The case of visual perception and emotion decoding[J]. bioRxiv, To be published.
[50]	PORCU M, OPERAMOLLA A, SCAPIN E, et al. Effects of white matter hyperintensities on brain connectivity and hippocampal volume in healthy subjects according to their localization[J]. Brain Connectivity, 2020, 10(8): 436–447. doi: 10.1089/brain.2020.0774
[51]	MARKETT S, JAWINSKI P, KIRSCH P, et al. Specific and segregated changes to the functional connectome evoked by the processing of emotional faces: A task-based connectome study[J]. Scientific Reports, 2020, 10(1): 4822. doi: 10.1038/s41598-020-61522-0
[52]	HUANG Pei, CARLIN J D, HENSON R N, et al. Improved motion correction of submillimetre 7T fMRI time series with boundary-based registration (BBR)[J]. NeuroImage, 2020, 210: 116542. doi: 10.1016/j.neuroimage.2020.116542
[53]	LINDQUIST M A. The statistical analysis of fMRI data[J]. Statistical Science, 2008, 23(4): 439–464. doi: 10.1214/09-STS282
[54]	SITARAM R, LEE S, RUIZ S, et al. Real-time support vector classification and feedback of multiple emotional brain states[J]. NeuroImage, 2011, 56(2): 753–765. doi: 10.1016/j.neuroimage.2010.08.007
[55]	SAARIMÄKI H, GOTSOPOULOS A, JÄÄSKELÄINEN I P, et al. Discrete neural signatures of basic emotions[J]. Cerebral Cortex, 2016, 26(6): 2563–2573. doi: 10.1093/cercor/bhv086
[56]	FRISTON K J, HOLMES A P, WORSLEY K J, et al. Statistical parametric maps in functional imaging: A general linear approach[J]. Human Brain Mapping, 1994, 2(4): 189–210. doi: 10.1002/hbm.460020402
[57]	KIM H C, BANDETTINI P A, and LEE J H. Deep neural network predicts emotional responses of the human brain from functional magnetic resonance imaging[J]. NeuroImage, 2019, 186: 607–627. doi: 10.1016/j.neuroimage.2018.10.054
[58]	ETHOFER T, VAN DE VILLE D, SCHERER K, et al. Decoding of emotional information in voice-sensitive cortices[J]. Current Biology, 2009, 19(12): 1028–1033. doi: 10.1016/j.cub.2009.04.054
[59]	FRISTON K J, FRITH C D, LIDDLE P F, et al. Functional connectivity: The principal-component analysis of large (PET) data sets[J]. Journal of Cerebral Blood Flow & Metabolism, 1993, 13(1): 5–14. doi: 10.1038/jcbfm.1993.4
[60]	ERYILMAZ H, VAN DE VILLE D, SCHWARTZ S, et al. Impact of transient emotions on functional connectivity during subsequent resting state: A wavelet correlation approach[J]. NeuroImage, 2011, 54(3): 2481–2491. doi: 10.1016/j.neuroimage.2010.10.021
[61]	ROY A K, SHEHZAD Z, MARGULIES D S, et al. Functional connectivity of the human amygdala using resting state fMRI[J]. NeuroImage, 2009, 45(2): 614–626. doi: 10.1016/j.neuroimage.2008.11.030
[62]	FRISTON K J, BUECHEL C, FINK G R, et al. Psychophysiological and modulatory interactions in neuroimaging[J]. NeuroImage, 1997, 6(3): 218–229. doi: 10.1006/nimg.1997.0291
[63]	TOMARKEN A J and WALLER N G. Structural equation modeling: Strengths, limitations, and misconceptions[J]. Annual Review of Clinical Psychology, 2005, 1: 31–65. doi: 10.1146/annurev.clinpsy.1.102803.144239
[64]	DESHPANDE G, LACONTE S, JAMES G A, et al. Multivariate granger causality analysis of fMRI data[J]. Human Brain Mapping, 2009, 30(4): 1361–1373. doi: 10.1002/hbm.20606
[65]	FRISTON K J, HARRISON L, and PENNY W. Dynamic causal modelling[J]. NeuroImage, 2003, 19(4): 1273–1302. doi: 10.1016/S1053-8119(03)00202-7
[66]	DESSEILLES M, SCHWARTZ S, DANG-VU T T, et al. Depression alters “top-down” visual attention: A dynamic causal modeling comparison between depressed and healthy subjects[J]. NeuroImage, 2011, 54(2): 1662–1668. doi: 10.1016/j.neuroimage.2010.08.061
[67]	BRÁZDIL M, MIKL M, MAREČEK R, et al. Effective connectivity in target stimulus processing: A dynamic causal modeling study of visual oddball task[J]. NeuroImage, 2007, 35(2): 827–835. doi: 10.1016/j.neuroimage.2006.12.020
[68]	DAVID O, MAESS B, ECKSTEIN K, et al. Dynamic causal modeling of subcortical connectivity of language[J]. Journal of Neuroscience, 2011, 31(7): 2712–2717. doi: 10.1523/JNEUROSCI.3433-10.2011
[69]	SLADKY R, HÖFLICH A, KÜBLBÖCK M, et al. Disrupted effective connectivity between the amygdala and orbitofrontal cortex in social anxiety disorder during emotion discrimination revealed by dynamic causal modeling for fMRI[J]. Cerebral Cortex, 2015, 25(4): 895–903. doi: 10.1093/cercor/bht279
[70]	TORRISI S J, LIEBERMAN M D, BOOKHEIMER S Y, et al. Advancing understanding of affect labeling with dynamic causal modeling[J]. NeuroImage, 2013, 82: 481–488. doi: 10.1016/j.neuroimage.2013.06.025
[71]	KUNCHEVA L I and RODRÍGUEZ J J. Classifier ensembles for fMRI data analysis: An experiment[J]. Magnetic Resonance Imaging, 2010, 28(4): 583–593. doi: 10.1016/j.mri.2009.12.021
[72]	CORTES C and VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273–297. doi: 10.1023/A:1022627411411
[73]	KARAMIZADEH S, ABDULLAH S M, HALIMI M, et al. Advantage and drawback of support vector machine functionality[C]. 2014 International Conference on Computer, Communications, and Control Technology, Langkawi, Malaysia, 2014: 63–65.
[74]	PANTAZATOS S P, TALATI A, PAVLIDIS P, et al. Decoding unattended fearful faces with whole-brain correlations: An approach to identify condition-dependent large-scale functional connectivity[J]. PLoS Computational Biology, 2012, 8(3): e1002441. doi: 10.1371/journal.pcbi.1002441
[75]	KOTZ S A, KALBERLAH C, BAHLMANN J, et al. Predicting vocal emotion expressions from the human brain[J]. Human Brain Mapping, 2013, 34(8): 1971–1981. doi: 10.1002/hbm.22041
[76]	AL-AIDAROOS K M, BAKAR A A, and OTHMAN Z. Naive bayes variants in classification learning[C]. 2010 International Conference on Information Retrieval & Knowledge Management, Shah Alam, Malaysia, 2010: 276–281.
[77]	NIGAM K, MCCALLUM A K, THRUN S, et al. Text classification from labeled and unlabeled documents using EM[J]. Machine Learning, 2000, 39(2): 103–134. doi: 10.1023/A:1007692713085
[78]	WAGER T D, KANG Jian, JOHNSON T D, et al. A bayesian model of category-specific emotional brain responses[J]. PLoS Computational Biology, 2015, 11(4): e1004066. doi: 10.1371/journal.pcbi.1004066
[79]	THIRION B, VAROQUAUX G, DOHMATOB E, et al. Which fMRI clustering gives good brain parcellations?[J]. Frontiers in Neuroscience, 2014, 8: 167. doi: 10.3389/fnins.2014.00167
[80]	HORIKAWA T, COWEN A S, KELTNER D, et al. The neural representation of visually evoked emotion is high-dimensional, categorical, and distributed across transmodal brain regions[J]. iScience, 2020, 23(5): 101060. doi: 10.1016/j.isci.2020.101060
[81]	COWEN A S and KELTNER D. Self-report captures 27 distinct categories of emotion bridged by continuous gradients[J]. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114(38): E7900–E7909. doi: 10.1073/pnas.1702247114
[82]	REYNOLDS D A. Gaussian Mixture Models[M]. LI S Z, JAIN A K. Encyclopedia of Biometrics. Boston: Springer, 2009659–663.
[83]	WILSON-MENDENHALL C D, BARRETT L F, and BARSALOU L W. Neural evidence that human emotions share core affective properties[J]. Psychological Science, 2013, 24(6): 947–956. doi: 10.1177/0956797612464242
[84]	AZARI B, WESTLIN C, SATPUTE A B, et al. Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience[J]. Scientific Reports, 2020, 10(1): 20284. doi: 10.1038/s41598-020-77117-8
[85]	OJA E. Principal components, minor components, and linear neural networks[J]. Neural Networks, 1992, 5(6): 927–935. doi: 10.1016/S0893-6080(05)80089-9
[86]	KANG T G and KIM N S. DNN-based voice activity detection with multi-task learning[J]. IEICE Transactions on Information and Systems, 2016, E99.D(2): 550–553. doi: 10.1587/transinf.2015EDL8168
[87]	NGUYEN A, YOSINSKI J, and CLUNE J. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 427–436.
[88]	YANG Zhengshi, ZHUANG Xiaowei, SREENIVASAN K, et al. A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory[J]. Medical Image Analysis, 2020, 60: 101622. doi: 10.1016/j.media.2019.101622
[89]	JANG H, PLIS S M, CALHOUN V D, et al. Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks[J]. NeuroImage, 2017, 145: 314–328. doi: 10.1016/j.neuroimage.2016.04.003
[90]	ZHAO Yu, DONG Qinglin, ZHANG Shu, et al. Automatic recognition of fMRI-derived functional networks using 3-D convolutional neural networks[J]. IEEE Transactions on Biomedical Engineering, 2018, 65(9): 1975–1984. doi: 10.1109/TBME.2017.2715281
[91]	KAYED M, ANTER A, and MOHAMED H. Classification of garments from fashion MNIST dataset using CNN LeNet-5 architecture[C]. 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE), Aswan, Egypt, 2020: 238–243.
[92]	ABD ALMISREB A, JAMIL N, and DIN N M. Utilizing AlexNet deep transfer learning for ear recognition[C]. 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP), Kota Kinabalu, Malaysia, 2018: 1–5.
[93]	SONG Zhenzhen, FU Longsheng, WU Jingzhu, et al. Kiwifruit detection in field images using faster R-CNN with VGG16[J]. IFAC-PapersOnLine, 2019, 52(30): 76–81. doi: 10.1016/j.ifacol.2019.12.500
[94]	CHUNG Y L, CHUNG H Y, and TSAI W F. Hand gesture recognition via image processing techniques and deep CNN[J]. Journal of Intelligent & Fuzzy Systems, 2020, 39(3): 4405–4418. doi: 10.3233/JIFS-200385
[95]	LEI Xusheng and SUI Zhehao. Intelligent fault detection of high voltage line based on the faster R-CNN[J]. Measurement, 2019, 138: 379–385. doi: 10.1016/j.measurement.2019.01.072
[96]	GUI Renzhou, CHEN Tongjie, and NIE Han. The impact of emotional music on active ROI in patients with depression based on deep learning: A task-state fMRI study[J]. Computational Intelligence and Neuroscience, 2019, 2019: 5850830. doi: 10.1155/2019/5850830
[97]	HARDOON D R, MOURAO-MIRANDA J, BRAMMER M, et al. Unsupervised analysis of fMRI data using kernel canonical correlation[J]. NeuroImage, 2007, 37(4): 1250–1259. doi: 10.1016/j.neuroimage.2007.06.017
[98]	CHANEL G, PICHON S, CONTY L, et al. Classification of autistic individuals and controls using cross-task characterization of fMRI activity[J]. NeuroImage:Clinical, 2016, 10: 78–88. doi: 10.1016/j.nicl.2015.11.010
[99]	SZYCIK G R, MOHAMMADI B, HAKE M, et al. Excessive users of violent video games do not show emotional desensitization: An fMRI study[J]. Brain Imaging and Behavior, 2017, 11(3): 736–743. doi: 10.1007/s11682-016-9549-y
[100]	ZOTEV V, MAYELI A, MISAKI M, et al. Emotion self-regulation training in major depressive disorder using simultaneous real-time fMRI and EEG neurofeedback[J]. NeuroImage:Clinical, 2020, 27: 102331. doi: 10.1016/j.nicl.2020.102331