Skip to main content
SpringerLink
Log in

A comprehensive review on GANs for time-series signals

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

During the last decade, deep learning (DL) techniques have demonstrated the capabilities in various applications with a large number of labeled samples. Unfortunately, it is normally difficult to obtain such large amounts of samples in practice. As one of the most promising research directions of data generation in DL, generative adversarial network (GAN) can process not only images but also time-series signals. Unfortunately, it is easy to lose the time-dependence information from the latter due to characteristics of GAN, which increases the challenge of signal generation. Besides, the existing evaluation methods cannot evaluate the performance of GAN comprehensively. Therefore, this paper summarizes the current work of time-series signals generation based on GAN and the existing evaluation methods of GAN. As compared to existing GAN-related review work, this paper claims four unique points: (1) we specify the difficulties of GAN for time-series generation, particularly for the biological signal generation with potential solutions; (2) we analyze drawbacks of existing evaluation methods, and propose feasible solutions; (3) some suggestions are provided for the further research of robust time-series signal generation, especially for biological signal generation; (4) we provide a preliminary experiment to demonstrate the effectiveness of GANs for time-series signals generation, particularly, electroencephalogram (EEG).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Ma Y, Wang Z, Yang H, Yang L (2020) Artificial intelligence applications in the development of autonomous vehicles: a survey. IEEE CAA J Autom Sin 11(3):315–329

    Article  Google Scholar 

  2. Xu LD, Duan L (2019) Big data for cyber physical systems in industry 4.0: a survey. Enterp IS 13(2):148–169

    MathSciNet  Google Scholar 

  3. Montes N, Rosillo N, Mora MC, Hilario L (2021) A novel real-time matlab/simulink/LEGO EV3 Platform for academic use in robotics and computer science. Sensors 21(3):1006

    Article  Google Scholar 

  4. Shhadat I, Al-bataineh B, Hayajneh A, Al-Sharif ZA (2020) The use of machine learning techniques to advance the detection and classification of unknown malware - sciencedirect. Procedia Comput Sci 170:917–922

    Article  Google Scholar 

  5. He H, Liu B, Luo H, Zhang T, Jiang J (2020) Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets. Stroke Vasc Neurol 5(4):381–387

    Article  Google Scholar 

  6. Karl E, Michel R (2018) Big data and artificial intelligence for diagnostic decision support in atypical dementia. Nervenarzt 89(8):875–884

    Article  Google Scholar 

  7. Huang C, Chen D, Guo W (2019) Innovation in methodology of education: big data and artificial intelligence. In: Processing of national conference on computer science technology and education 49–60

  8. Gordon SG, Robert H, Slavko Z (2020) Computer science meets education: natural language processing for automatic grading of open-ended questions in eBooks. J Educ Comput Res 58(3):1227–1255

    Google Scholar 

  9. Bishop CM, Nasrabadi NM (2007) Pattern recognition and machine learning. J Electron Imaging 16(4):049901

    Article  Google Scholar 

  10. Trentin E, Schwenker F, El Gayar N, Abbas HM (2018) Off the mainstream: advances in neural networks and machine learning for pattern recognition. Neural Process Lett 48(2):643–648

    Article  Google Scholar 

  11. Rao DJ (2019) Big data and artificial intelligence. Hoboken, New Jersey

    Book  Google Scholar 

  12. LeCun Y, Bengio Y, Hinton GE (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  13. Zhang X, Yao L, Wang X, Monaghan JJM, Mcalpine D, Zhang Y (2020) A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. J Neural Eng 18(3):1–44

    Google Scholar 

  14. Sakhavi S, Guan C, Yan S (2018) Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks. IEEE Transac Neural Netw Learn Syst 29(11):5619–5629

    Article  MathSciNet  Google Scholar 

  15. Shao Y, Hardmeier C, Tiedemann J, Nivre J (2017) Character-based joint segmentation and POS tagging for chinese using bidirectional RNN-CRF. In: Processing of international joint conference on natural language processing (IJCNLP) pp. 173–183

  16. Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Processing of the North American chapter of the association for computational linguistics – human language technologies (NAACL-HLT) pp 4171–4186

  17. Zhang D, Yao L, Zhang X, Wang S, Boots R (2018) Cascade and parallel convolutional recurrent neural networks on EEG⁃based intention recognition for brain computer interface. In: Proceedings of the 32nd AAAI conference on artificial intelligence (AAAI) pp. 1703–1710

  18. Song T, Zheng W, Song P, Cui Z (2020) EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans Affect Comput 11(3):532–541

    Article  Google Scholar 

  19. Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D (2016) Mastering the game of Go with deep neural networks and tree search. Nature 7587(2016):484–489

    Article  Google Scholar 

  20. Moudík J, Roman N (2016) Determining player skill in the game of go with deep neural networks. In: Proceedings of theory and practice of natural computing pp. 188–195

  21. Kazeminia S, Baur C, Kuijper A, van Ginneken B, Navab N, Albarqouni S, Mukhopadhyay A (2018) GANs for medical image analysis. arXiv preprint arXiv:1809.06222

  22. Zhang H, Xia L, Song R, Yang J, Hao H, Liu J, Zhao Y (2020) Cerebrovascular segmentation in MRA via reverse edge attention network. In: Processing of medical image computing & computer assisted intervention (MICCAI) pp. 66–75

  23. Higy B, Bell P (2018) Few-shot learning with attention-based sequence-to-sequence models. arXiv preprint arXiv:1811.03519

  24. Kadam S, Vaidya V (2018) Review and analysis of zero, one and few shot learning approaches. In: Processing of the 18th international conference on intelligent systems design and applications (ISDA’18) pp. 100–112

  25. Santoro A, Bartunov S, Botvinick M, Wierstra D, Lillicrap TP (2016) One-shot learning with memory-augmented neural networks. arXiv preprint arXiv:1605.06065

  26. Munkhdalai T, Yu H (2017) Meta networks. In: Processing of the 34th international conference on machine learning (ICML’17) pp. 2554–2563

  27. Ravi S, Larochelle H (2017) Optimization as a model for few-shot learning. In: Processing of the 5th international conference on learning representations (ICLR’17) pp. 24–26

  28. Kingma DP, Welling M (2014) Auto-encoding variational bayes. In: Processing of the 2nd international conference on learning representations (ICLR’14) pp.1–14

  29. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville AC, Bengio Y (2014) Generative adversarial nets. In: Processing of the 27th neural information processing systems: annual conference on neural information processing systems (NeurIPS’14) pp. 2672–2680

  30. Kingma DP, Dhariwal P (2018) Glow: generative flow with invertible 1x1 convolutions. In: Processing of the31th neural information processing systems: annual conference on neural information processing systems (NeurIPS’18) pp. 10236–10245

  31. Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M (2014) Domain-adversarial neural networks. arXiv preprint arXiv:1412.4446

  32. Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D (2017) Unsupervised pixel-level domain adaptation with generative adversarial networks. In: Processing of IEEE conference on computer vision and pattern recognition (CVPR’17) pp. 95–104

  33. Kim T, Cha M, Kim H, Lee JK, Kim J (2017) Learning to discover cross-domain relations with generative adversarial networks. In: Proceedings of the 34th international conference on machine learning (ICML’17) pp. 1857–1865

  34. Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, Aitken AP, Tejani A, Totz J, Wang Z, Shi W (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Processing of the IEEE conference on computer vision and pattern recognition (CVPR’17) pp. 105–114

  35. Wang C, Xu C, Wang C, Tao D (2018) Perceptual adversarial networks for image-to-image transformation. IEEE Trans Image Process 27(8):4066–4079

    Article  MathSciNet  Google Scholar 

  36. Wu J, Zhang C, Xue T, Freeman B, Tenenbaum J (2016) Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. In: Processing of the 29th neural information processing systems: annual conference on neural information processing systems (NeurIPS’16) pp. 82–90

  37. Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Processing of the IEEE international conference on computer vision (ICCV’17) pp. 2242–2251

  38. Tulyakov S, Ming-Yu L, Yang X, Kautz J (2018) Mocogan: decomposing motion and content for video generation. In: Processing of IEEE international conference on computer vision (ICCV’18) pp. 1526–1535

  39. Vondrick C, Pirsiavash H, Torralba A (2016) Generating videos with scene dynamics. In: Processing of the 29th neural information processing systems: annual conference on neural information processing systems (NeurIPS’16) pp. 613–621

  40. Walker J, Marino K, Gupta A, Hebert M (2017) The pose knows: video forecasting by generating pose futures. In: Processing of the ieee international conference on computer vision (ICCV’17) pp. 3352–3361

  41. van den Oord A, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior AW, Kavukcuoglu K (2016) WaveNet: a generative model for raw audio. In: Processing of the 9th isca speech synthesis workshop, Sunnyvale (ISCA’16) pp. 125–140

  42. Guimaraes GL, Sanchez-Lengeling B, Farias PLC, Aspuru-Guzik A (2017) Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models. arXiv preprint arXiv:1705.10843

  43. Lee S, Hwang U, Min S, Yoon S (2017) A SeqGAN for polyphonic music generation. arXiv preprint arXiv:1710.11418

  44. Hsu CC, Hwang HT, Wu YC, Tsao Y, Wang HM (2017) Voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks. In: Processing of the 18th annual conference of the international speech communication association (INTERSPEECH’17) pp. 3364–3368

  45. Lin K, Li D, He X, Sun MT, Zhang Z (2017) Adversarial ranking for language generation. Inl Processing of the 30th neural information processing systems: annual conference on neural information processing systems (NeurIPS’17) pp. 3155–3165

  46. Dai W, Dong N, Wang Z, Liang X, Zhang H, Xing EP (2018) SCAN: structure correcting adversarial network for organ segmentation in chest X-ray. In: Processing of the medical image analysis-and-multimodal learning for clinical decision support - 4th international workshop (DLMIA’18), and 8th international workshop (ML-CDS’18), Held in Conjunction with MICCAI pp. 263–273

  47. Mardani M, Gong E, Cheng JY, Vasanawala S, Zaharchuk G, Alley MT, Thakur N, Han S, Dally WJ, Pauly JM, Xing L (2017) Deep generative adversarial networks for compressed sensing automates MRI. arXiv preprint arXiv:1706.00051

  48. Yang D, Xiong T, Xu D, Huang Q, Liu D, Zhou SK, Xu Z, Park JH, Chen M, Tran TD, Chin SP, Metaxas DN, Comaniciu D (2017) Automatic vertebra labeling in large-scale 3d CT using deep image-to-image network with message passing and sparsity regularization. In: Processing of the 25th international conference information processing in medical imaging (IPMI’17) pp. 633–644

  49. Shi H, Dong J, Wang W, Qian Y, Zhang X (2017) SSGAN: secure steganography based on generative adversarial networks. In: Processing of the advances in multimedia information processing - 18th Pacific-rim conference on multimedia (PCM) pp. 534–544

  50. Volkhonskiy D, Nazarov I, Borisenko B, Burnaev E (2017) Steganographic generative adversarial networks. arXiv preprint arXiv:1703.05502

  51. Liu LL, Zhang HJ, Xu XF, Zhang Z, Yan SC (2020) Collocating clothes with generative adversarial networks cosupervised by categories and attributes: a multidiscriminator framework. IEEE Trans Neural Netw Learn Syst 31(9):3540–3554

    Article  MathSciNet  Google Scholar 

  52. Zhang HJ, Wang XH, Liu LL, Zhou DL, Zhang Z (2020) WarpClothingOut: a stepwise framework for clothes translation from the human body to tiled images. IEEE Multimed 27(4):58–68

    Article  Google Scholar 

  53. Pano-Azucena AD, Tlelo-Cuautle E, Ovilla-Martinez B, Gerardo de la Fraga L, Li R (2020) Pipeline FPGA-based implementations of ANNs for the Prediction of up to 600-steps-ahead of chaotic time series. J Circuits Syst Comput 30(9):1142–1153

    Google Scholar 

  54. Brock A, Donahue J, Simonyan K (2019) Large scale GAN training for high fidelity natural image synthesis. In: Processing of the 7th international conference on learning representations (ICLR’19)

  55. Huang SW, Lin CT, Chen SP, Wu YY, Hsu PH, Lai SH (2018) AugGAN: cross domain adaptation with GAN-based data augmentation. In: Processing of the 15th European conference computer vision (ECCV’18) pp. 731–744

  56. Wang X, Yu K, Wu S, Gu J, LiuY, Dong C, Qiao Y, Loy CC (2018) ESRGAN: enhanced super-resolution generative adversarial networks. In: Processing of the 15th european conference computer vision (ECCV’18) pp. 63–79

  57. Shaham TR, Dekel T, Michaeli T (2019) SinGAN: learning a generative model from a single natural image. In: Processing of the IEEE international conference on computer vision (ICCV’19) pp. 1–11

  58. Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-toimage translation using cycle-consistent adversarial networks. In: Processing of the IEEE international conference on computer Vision (ICCV’19) pp. 2242–2251

  59. Mogren O (2016) C-RNN-GAN: continuous recurrent neural networks with adversarial training. arXiv preprint arXiv:1611.09904

  60. Donahue C, McAuley JJ, Puckette MS (2019) Adversarial audio synthesis. In: Processing of the 7th international conference on learning representations (ICLR’19)

  61. Lee CY, Toffy A, Jung GJ, Han WJ (2018) Conditional wavegan. arXiv preprint arXiv:1809.10636

  62. Engel JH, Agrawal KK, Chen S, Gulrajani I, Donahue C, Roberts A (2019) Gansynth: adversarial neural audio synthesis. In: Processing of the 7th international conference on learning representations (ICLR’19)

  63. Pascual S, Bonafonte A, Serra J (2017) SEGAN: speech enhancement generative adversarial network. In: Processing of the 18th annual conference of the international speech communication association (ISCA’17) pp. 3642–3646

  64. Hsu CC, Hwang HT, Wu YC, Tsao Y, Wang HM (2017) Voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks. In: Processing of the 18th annual conference of the international speech communication association (ISCA’17) pp. 3364–3368

  65. Abdelfattah SM, Abdelrahman GM, Wang M (2018) Augmenting the size of EEG datasets using generative adversarial networks. In: Processing of the international joint conference on neural networks (IJCNN’18) pp. 1–6

  66. Karimian N, Guo Z, Tehranipoor MM, Forte D (2017) Highly reliable key generation from electrocardiogram (ECG). IEEE Trans Biomed Eng 64(6):1400–1411

    Article  Google Scholar 

  67. Palazzo S, Spampinato C, Kavasidis I, Giordano D, Shah M (2017) Generative adversarial networks conditioned by brain signals. In: Processing of the IEEE international conference on computer vision (ICCV’17) pp. 3430–3438

  68. Wang F, Zhong S, Peng J, Jiang J, Liu Y (2018) Data augmentation for eeg-based emotion recognition with deep convolutional neural networks. In: Processing of the 24th international conference multimedia modeling (MMM’18) pp. 82–93

  69. Mabu S, Fujita K, Kuremoto T (2019) Disaster area detection from synthetic aperture radar images using convolutional autoencoder and one-class SVM. J Robot Netw Artif Life 6(1):48–51

    Article  Google Scholar 

  70. Lopez-Paz D, Oquab M (2017) Revisiting classifier two-sample tests for GAN evaluation and causal discovery. In: Processing of the 5th international conference on learning representations (ICLR’17)

  71. Ma T (2018) Generalization and equilibrium in generative adversarial nets (GANs) (invited talk). In: Proceedings of the 50th Annual ACM SIGACT symposium on theory of computing (STOC’18) p. 2

  72. Barratt S, Sharma R (2018) A note on the inception score. arXiv preprint arXiv:1801.01973.

  73. Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: Processing of the 30th neural information processing systems: annual conference on neural information processing systems (NeurIPS’17) pp. 6626–6637

  74. Slimene A, Zagrouba E (2013) Kernel maximum mean discrepancy for region merging approach. In: Processing of the 15th international conference computer analysis of images and patterns (CAIP’13) pp. 475–482

  75. Wang L, Zhang Y, Feng J (2005) On the Euclidean distance of images. IEEE Trans Pattern Anal Mach Intell 27(8):1334–1339

    Article  Google Scholar 

  76. Govindarajan M, Chandrasekaran RM (2010) Evaluation of k-nearest neighbor classififier performance for direct marketing. Expert Syst Appl 37(1):253–258

    Article  Google Scholar 

  77. Shmelkov K, Schmid C, Alahari K (2018) How good is my gan?. In: Processing of the 15th european conference computer vision (ECCV’18) pp. 218–234

  78. Wang K, Gou C, Duan Y, Lin Y, Zheng X, Wang F-Y (2017) Generative adversarial networks: introduction and outlook. IEEE CAA J Autom Sin 4(4):588–598

    Article  MathSciNet  Google Scholar 

  79. Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35(1):53–65

    Article  Google Scholar 

  80. Hong Y, Hwang U, Yoo J, Yoon S (2019) How generative adversarial networks and their variants work: an overview. ACM Comput Surv 52(1):10:1-10:43

    Google Scholar 

  81. Wang Z, She Q, Ward TE (2019) Generative adversarial networks: a survey and taxonomy. arXiv preprint arXiv:1906.01529

  82. Hitawala S (2018) Comparative study on generative adversarial networks. arXiv preprint arXiv:1801.04271

  83. Zamorski M, Zdobylak A, Zieba M, Swiatek J (2019) Generative adversarial networks: recent developments. In: Processing of the 18th international conference artificial intelligence and soft computing (ICAISC’19) pp. 248–258

  84. Pan Z, Weijie Yu, Yi X, Khan A, Yuan F, Zheng Y (2019) Recent progress on generative adversarial networks (GANs): a survey. IEEE Access 7(2019):36322–36333

    Article  Google Scholar 

  85. Engel JH, Resnick C, Roberts A, Dieleman S, Norouzi M, Eck D, Simonyan K (2017) Neural audio synthesis of musical notes with WaveNet autoencoders. In: Processing of the 34th international conference on machine learning (ICML’17) pp. 1068–1077

  86. Hendrix GG, Carbonell JG (1981) A tutorial on natural-language processing. In: Processing of the ACM 1981 annual conference pp. 4–8

  87. Wu Z, Li H (2013) Voice conversion and spoofing attack on speaker verification systems. In: Processing of the IEEE Asia-Pacific signal and information processing association annual summit and conference (APSIPA’13) pp. 1–9

  88. Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. In: Processing of the 4th international conference on learning representations (ICLR’16)

  89. Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of GANs for improved quality, stability, and variation. In: Processing of the 6th international conference on learning representations (ICLR’18)

  90. Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784

  91. Mao X, Li Q, Xie H, Lau RYK, Wang Z, Smolley SP (2017) Least squares generative adversarial networks. In: Processing of the IEEE international conference on computer vision (ICCV’17) pp. 2813–2821

  92. Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of wasserstein GANs. In: Processing of the 30th neural information processing systems: annual conference on neural information processing systems (NeurIPS’17) pp. 5767–5777

  93. Dosovitskiy A, Springenberg JT, Brox T (2015) Learning to generate chairs with convolutional neural networks. In: Processing of the IEEE conference on computer vision and pattern recognition (CVPR’15) pp. 1538–1546

  94. Li D, Chen D, Shi L, Jin B, Goh J, Ng SK (2019) MAD-GAN: multivariate anomaly etection for time series data with generative adversarial networks. In: Processing of the international conference on artificial neural networks (ICANN’19) pp. 703–716

  95. Bashar MA, Nayak R (2020) TAnoGAN: time series anomaly detection with generative Adversarial networks. arXiv:2008.09567v1

  96. Geiger A, Liu D, Alnegheimish S, Cuesta-Infanteet A, Veeramachaneni K (2020) TadGAN: time series anomaly detection using generative adversarial networks. In: IEEE international conference on big data 978-1-7281-6251

  97. Schlegl T, Seebock P, Waldstein SM, Schmidt-Erfurth U, Langs G (2017) Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), 10265 LNCS:146–147

  98. Zenati H, Foo CS, Lecouat B, Manek G, Chandrasekhar VR (2018) Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222

  99. Akcay S, Atapour-Abarghouei A, Breckon TP (2019) GANomaly: semi-supervised anomaly detection via adversarial training. In: Processing of Asian conference on computer vision (ACCV’19) pp. 622–63

  100. Yu L, Zhang W, Wang J, Yu Y (2017) SeqGAN: sequence generative adversarial nets with policy gradient. In: Processing of the 31th AAAI conference on artificial intelligence (AAAI’17) pp. 2852–2858

  101. Dong HW, Hsiao WY, Yang LC, Yang YH (2018) MuseGAN: multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In: Processing of the 32th AAAI conference on artificial intelligence (AAAI’18) pp. 34–41

  102. Martin E, Cundy C (2017) Parallelizing linear recurrent neural nets over sequence length. arXiv preprint arXiv:1709.04057

  103. Huang Y, Huang X, Cai Q (2018) Music generation based on convolution-LSTM. Comput Inf Sci 11(3):50–56

    Google Scholar 

  104. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  105. Sutton RS, McAllester DA, Singh SP, Mansour Y (1999) Policy gradient methods for reinforcement learning with function approximation. In: Processing of the 30th neural information processing systems: annual conference on neural information processing systems (NeurIPS’99) pp. 1057–1063

  106. Falkowski BJ (2020) Maximum likelihood estimates and a Kernel k-means iterative algorithm for normal mixtures. In: Processing of the IEEE industrial electronics society (IECON’20) pp. 2115–2118

  107. Hartmann KG, Schirrmeister RT, Ball T (2018) EEG-GAN: generative adversarial networks for electroencephalograhic (EEG) brain signals. arXiv preprint arXiv:1806.01875

  108. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein GAN. arXiv preprint arXiv:1701.07875

  109. Luo Y, Lu BL (2018) EEG data augmentation for emotion recognition using a conditional wasserstein GAN. In: Processing of the 13th IEEE engineering in medicine and biology society (EMBS) pp. 2535–2538

  110. Che T, Li Y, Jacob AP, Panwar S, Rad P, Jung TP, Huang Y (2019) Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Events. arXiv preprint arXiv:1911.04379

  111. Fiesler E, Caulfield HJ, Choudry A, Ryan JP (1990) Maximum capacity topologies for fully connected layered neural networks with bidirectional connections. In: Processing of the international joint conference on neural networks (IJCNN’90) pp. 827–831

  112. Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: Processing of the 3th international conference on learning representations (ICLR’15)

  113. Weiss A, Yeredor A (2019) A maximum likelihood-based minimum mean square error separation and estimation of stationary Gaussian sources from noisy mixtures. IEEE Trans Signal Process 67(19):5032–5045

    Article  MathSciNet  Google Scholar 

  114. Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR (2004) BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng 51(6):1034–1043

    Article  Google Scholar 

  115. Sun S, Zhou J (2014) A review of adaptive feature extraction and classification methods for eeg-based brain-computer interfaces. In: Processing of the international joint conference on neural networks (IJCNN’14) pp. 1746–1753

  116. Bińkowski M, Donahue J, Dieleman S, Clark A, Elsen E, Casagrande N, Cobo LC, Simonyan K (2020) High fidelity speech synthesis with adversarial networks. arXiv preprint arXiv:1377.11646

  117. Theis L, van den Oord A, Bethge M (2016) A note on the evaluation of generative models. In: Processing of the 4th international conference on learning representations (ICLR’16)

  118. Xu Q, Huang G, Yuan Y, Guo C, Sun Y, Wu F, Weinberger KQ (2018) An empirical study on evaluation metrics of generative adversarial networks. arXiv preprint arXiv:1806.07755

  119. Lucic M, Kurach K, Michalski M, Gelly S, Bousquet O (2017) Are gans created equal? A large-scale study. arXiv preprint arXiv:1711.10337

  120. Onishi K, Imai H (1997) Voronoi diagram in statistical parametric space by kullback-leibler divergence. In: Processing of the 13th annual symposium on computational geometry (2017) pp. 463–465

  121. Weinberg GV, Glenny VG (2016) Optimal rayleigh approximation of the k-distribution via the kullback-leibler divergence. IEEE Signal Process Lett 23(8):1067–1070

    Article  Google Scholar 

  122. Otto F, Westdickenberg M (2005) Eulerian calculus for the contraction in the wasserstein distance. SIAM J Math Anal 37(4):1227–1255

    Article  MathSciNet  Google Scholar 

  123. Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Processing of the IEEE conference on computer vision and pattern recognition (CVPR’15) pp. 1–9

  124. Wang Z, Lyu S, Liu L (2019) Learnable markov chain monte carlo sampling methods for lattice Gaussian distribution. IEEE Access 7(2019):87494–87503

    Article  Google Scholar 

  125. Kowalczyk A (2000) Sparsity of data representation of optimal kernel machine and leave-one-out estimator. In: Processing of the 31th neural information processing systems: annual conference on neural information processing systems (NeurIPS’99) pp. 252–258

  126. Reddi SJ, Ramdas A, Póczos B, Singh A, Wasserman LA (2014) Kernel MMD, the median heuristic and distance correlation in high dimensions. arXiv preprint arXiv:1406.2083

  127. Korczowski L, Cederhout M, Andreev A, Cattan G, Congedo M (2019) Brain Invaders calibration-less P300-based BCI with modulation of flash duration Dataset (bi2015a). [Research Report] GIPSA-lab. hal-02172347

  128. Gurumurthy S, Kiran Sarvadevabhatla R, Venkatesh Babu R (2017) DeLiGAN: generative adversarial networks for diverse and limited data. In: Processing of the IEEE conference on computer vision and pattern recognition (CVPR’17) pp. 4941–4949

  129. Bengio Y, Li W (2017) Mode regularized generative adversarial networks. In: Processing of the 5th international conference on learning representations (ICLR’17)

  130. Buzano R, Rupflin M (2015) Smooth long-time existence of Harmonic Ricci Flow on surfaces. J Lond Math Soc 95(1):277–304

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work is supported by Beijing Natural Science Foundation (4202011), National Natural Science Foundation of China (61572076, 61772351) and Key Research Grant of Academy for Multidisciplinary Studies of CNU (JCKXYJY2019018).

Author information

Author notes

  1. Likun Xia and Da Zhang contributed equally to the work.

Authors and Affiliations

  1. College of Information Engineering, Capital Normal University, Beijing, China

    Da Zhang & Likun Xia

  2. International Science and Technology Cooperation Base of Electronic System Reliability and Mathematical Interdisciplinary, Capital Normal University, Beijing, China

    Da Zhang & Likun Xia

  3. Laboratory of Neural Computing and Intelligent Perception, Capital Normal University, Beijing, China

    Da Zhang & Likun Xia

  4. Department of Computer Science, Winona State University, Winona, USA

    Ming Ma

  5. Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing, China

    Likun Xia

Authors
  1. Da Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Likun Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Likun Xia.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest to this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, D., Ma, M. & Xia, L. A comprehensive review on GANs for time-series signals. Neural Comput & Applic 34, 3551–3571 (2022). https://doi.org/10.1007/s00521-022-06888-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-06888-0

Keywords

Search

Navigation