Advanced search
Publication Cover
Brain-Apparatus Communication: A Journal of Bacomics Volume 2, 2023 - Issue 1
Submit an article Journal homepage
2,251
Views
1
CrossRef citations to date
0
Altmetric
Review Articles

A survey of deep learning-based classification methods for steady-state visual evoked potentials

Yudong Pana School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, P.R. ChinaORCID Iconhttps://orcid.org/0000-0002-3209-4721View further author information
ORCID Icon,
Jianbo Chena School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, P.R. ChinaORCID Iconhttps://orcid.org/0000-0001-6624-8381View further author information
ORCID Icon &
Yangsong Zhanga School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, P.R. China;b MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, P.R. ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-6764-3567View further author information
ORCID Icon
Article: 2181102 | Received 08 Dec 2022, Accepted 10 Feb 2023, Published online: 03 Mar 2023

Reference

  • Wolpaw JR, Birbaumer N, McFarland DJ, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002;113(6):767–791.
  • Yao D, Qin Y, Zhang Y. From psychosomatic medicine, brain–computer interface to brain–apparatus communication. Brain-Apparatus Comm. 2022;1(1):66–88.
  • Wolpaw JR. Brain-computer interfaces (BCIs) for communication and control Proceedings of the 9th international ACM SIGACCESS conference on Computers and accessibility. 2007. p. 1–2.
  • Qi F, Li Y, Wu W. RSTFC: a novel algorithm for spatio-temporal filtering and classification of single-trial EEG. IEEE Trans. Neural Netw Learning Syst. 2015;26(12):3070–3082.
  • Jin J, Zhang H, Daly I, et al. An improved P300 pattern in BCI to catch user’s attention. J Neural Eng. 2017;14(3):036001.
  • Jiao Y, Zhang Y, Wang Y, et al. A novel multilayer correlation maximization model for improving CCA-based frequency recognition in SSVEP brain–computer interface. Int. J. Neur. Syst. 2018;28(04):1750039.
  • Zhang Y, Xu P, Liu T, et al. Multiple frequencies sequential coding for SSVEP-based brain-computer interface. PLOS One. 2012;7(3):e29519.
  • Chen X, Chen Z, Gao S, et al. A high-itr ssvep-based bci speller. Brain-Computer Interfaces. 2014;1(3-4):181–191.
  • Yin E, Zhou Z, Jiang J, et al. A dynamically optimized SSVEP brain–computer interface (BCI) speller. IEEE Trans Biomed Eng. 2015;62(6):1447–1456.
  • Nakanishi M, Wang Y, Wang YT, et al. A high-speed brain speller using steady-state visual evoked potentials. Int J Neur Syst. 2014;24(06):1450019.
  • Cheng M, Gao X, Gao S, et al. Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans Biomed Eng. 2002;49(10):1181–1186.
  • Wang Y, Wang R, Gao X, et al. A practical VEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng. 2006;14(2):234–240.
  • Lin Z, Zhang C, Wu W, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng. 2006;53(12 Pt 2):2610–2614.
  • Zhang Y, Xu P, Cheng K, et al. Multivariate synchronization index for frequency recognition of SSVEP-based brain–computer interface. J Neurosci Methods. 2014;221:32–40.
  • Bin G, Gao X, Wang Y, et al. A high-speed BCI based on code modulation VEP. J Neural Eng. 2011;8(2):025015.
  • Chen Y, Yang C, Ye X, et al. Implementing a calibration-free SSVEP-based BCI system with 160 targets. J Neural Eng. 2021;18(4):046094.
  • Nakanishi M, Wang Y, Chen X, et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Trans Biomed Eng. 2018;65(1):104–112.
  • Zhang Y, Guo D, Li F, et al. Correlated component analysis for enhancing the performance of SSVEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng. 2018;26(5):948–956.
  • Zhang Y, Yin E, Li F, et al. Two-stage frequency recognition method based on correlated component analysis for SSVEP-based BCI. IEEE Trans Neural Syst Rehabil Eng. 2018;26(7):1314–1323.
  • Wong CM, Wan F, Wang B, et al. Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs. J Neural Eng. 2020;17(1):016026.
  • He K, Zhang X, Ren S, et al. Deep residual learning for image recognition Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 770–778.
  • Devlin J, Chang MW, Lee K, et al. Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
  • Berg R, Kipf TN, Welling M. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263, 2017.
  • Faust O, Hagiwara Y, Hong TJ, et al. Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Programs Biomed. 2018;161:1–13.
  • Roy Y, Banville H, Albuquerque I, et al. Deep learning-based electroencephalography analysis: a systematic review. J. Neural Eng. 2019;16(5):051001.
  • Craik A, He Y, Contreras-Vidal JL. Deep learning for electroencephalogram (EEG) classification tasks: a review. J Neural Eng. 2019;16(3):031001.
  • Zhang X, Yao L, Wang X, et al. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. J Neural Eng. 2021;18(3):031002.
  • Zhang Y, Cai H, Nie L, et al. An end-to-end 3D convolutional neural network for decoding attentive mental state. Neural Netw. 2021;144:129–137.
  • Lawhern VJ, Solon AJ, Waytowich NR, et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J. Neural Eng. 2018;15(5):056013.
  • Nakanishi M, Wang Y, Wang YT, et al. A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials. PLOS One. 2015;10(10):e0140703.
  • Waytowich N, Lawhern VJ, Garcia JO, et al. Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials. J. Neural Eng. 2018;15(6):066031.
  • Aznan NKN, Bonner S, Connolly J, et al. On the classification of SSVEP-based dry-EEG signals via convolutional neural networks. 2018 IEEE international conference on systems Man, and Cybernetics (SMC). Miyazaki, Japan, 07-10 October 2018. pp. 3726–3731.
  • Nguyen TH, Chung WY. A single-channel SSVEP-based BCI speller using deep learning. IEEE Access. 2019;7:1752–1763.
  • Zhang R, Xu Z, Zhang L, et al. The effect of stimulus number on the recognition accuracy and information transfer rate of SSVEP–BCI in augmented reality. J Neural Eng. 2022;19(3):036010.
  • Zhao X, Liu C, Xu Z, et al. SSVEP stimulus layout effect on accuracy of brain-computer interfaces in augmented reality glasses. IEEE Access. 2020;8:5990–5998.
  • Zhao X, Du Y, Zhang R. A CNN-based multi-target fast classification method for AR-SSVEP. Comput Biol Med. 2022;141:105042.
  • Khok HJ, Koh VTC, Guan C. Deep multi-task learning for SSVEP detection and visual response mapping. 2020 IEEE international conference on systems man, and cybernetics (SMC). Toronto, ON, Canada, 11–14 October 2020; pp. 1280–1285.
  • Wang Y, Chen X, Gao X, et al. A benchmark dataset for SSVEP-based brain–computer interfaces. IEEE Trans Neural Syst Rehabil. Eng. 2017;25(10):1746–1752.
  • Attia M, Hettiarachchi I, Hossny M, et al. A time domain classification of steady-state visual evoked potentials using deep recurrent-convolutional neural networks. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). Washington, DC, USA, 04-07 April 2018. pp. 766–769.
  • Ishizuka K, Kobayashi N, Saito K. High accuracy and short delay 1ch-ssvep quadcopter-bmi using deep learning. JRM. 2020;32(4):738–744.
  • Pan Y, Chen J, Zhang Y, et al. An efficient CNN-LSTM network with spectral normalization and label smoothing technologies for SSVEP frequency recognition. J. Neural Eng. 2022;19(5):056014.
  • Mahmood M, Mzurikwao D, Kim YS, et al. Fully portable and wireless universal brain–machine interfaces enabled by flexible scalp electronics and deep learning algorithm. Nat Mach Intell. 2019;1(9):412–422.
  • Thomas J, Maszczyk T, Sinha N, et al. Deep learning-based classification for brain-computer interfaces. 2017 IEEE international conference on systems man, and cybernetics (SMC). Banff, AB, Canada, 05-08 October 2017. pp. 234–239.
  • Chen X, Wang Y, Gao S, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface. J. Neural Eng. 2015;12(4):046008.
  • Cecotti H. A time–frequency convolutional neural network for the offline classification of steady-state visual evoked potential responses. Pattern Recog Lett. 2011;32(8):1145–1153.
  • Kwak NS, Müller KR, Lee SW. A convolutional neural network for steady state visual evoked potential classification under ambulatory environment. PLoS One. 2017;12(2):e0172578.
  • Ravi A, Manuel J, Heydari N, et al. A convolutional neural network for enhancing the detection of SSVEP in the presence of competing stimuli. 2019 41st annual international conference of the ieee engineering in medicine and biology society (EMBC). Berlin, Germany, 23-27 July 2019. pp. 6323–6326.
  • Ravi A, Beni NH, Manuel J, et al. Comparing user-dependent and user-independent training of CNN for SSVEP BCI. J. Neural Eng. 2020;17(2):026028.
  • Xing J, Qiu S, Ma X, et al. A CNN-based comparing network for the detection of steady-state visual evoked potential responses. Neurocomputing. 2020;403:452–461.
  • Li Y, Xiang J, Kesavadas T. Convolutional correlation analysis for enhancing the performance of SSVEP-based brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 2020;28(12):2681–2690.
  • Zhang X, Qiu S, Geng M, et al. Enhancing detection of SSVEPs for high-speed brain-computer interface with a Siamese architecture. 2021 IEEE international conference on bioinformatics and biomedicine (BIBM). Houston, TX, USA,09-12 December 2021.pp. 1623–1627.
  • Zhang X, Qiu S, Zhang Y, et al. Bidirectional siamese correlation analysis method for enhancing the detection of SSVEPs. J Neural Eng. 2022;19(4):046027.
  • Xiao X, Xu L, Yue J, et al. Fixed template network and dynamic template network: novel network designs for decoding steady-state visual evoked potentials. J Neural Eng. 2022;19(5):056049.
  • Dang W, Li M, Lv D, et al. MHLCNN: multi-harmonic linkage CNN model for SSVEP and SSMVEP signal classification. IEEE Trans Circuits Syst II Express Briefs. 2021;69(1):244–248.
  • Zhao D, Wang T, Tian Y, et al. Filter bank convolutional neural network for SSVEP classification. IEEE Access. 2021;9:147129–147141.
  • Ding W, Shan J, Fang B, et al. Filter bank convolutional neural network for short time-window steady-state visual evoked potential classification. IEEE Trans. Neural Syst. Rehabil. Eng. 2021;29:2615–2624.
  • Yao H, Liu K, Deng X, et al. FB-EEGNet: a fusion neural network across multi-stimulus for SSVEP target detection. J Neurosci Methods. 2022;379:109674.
  • Bassi PRAS, Attux R. FBDNN: filter banks and deep neural networks for portable and fast brain-computer interfaces. Biomed Phys Eng Express. 2022;8(3):035018.
  • Liu B, Huang X, Wang Y, et al. BETA: a large benchmark database toward SSVEP-BCI application. Front. Neurosci. 2020;14:627.
  • Zhu F, Jiang L, Dong G, et al. An open dataset for wearable ssvep-based brain-computer interfaces. Sensors. 2021;21(4):1256.
  • Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In Advances in Neural Information Processing Systems, 5998–6008, 2017.
  • Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
  • Sun J, Xie J, Zhou H. EEG classification with transformer-based models. 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech). Nara, Japan, 09-11 March 2021. pp. 92-93. .
  • Song Y, Jia X, Yang L, et al. Transformer-based spatial-temporal feature learning for EEG decoding. arXiv preprint arXiv:2106.11170, 2021.
  • Liu J, Zhang L, Wu H, et al. Transformers for EEG emotion recognition. arXiv preprint arXiv:2110.06553, 2021.
  • Chen J, Zhang Y, Pan Y, et al. A Transformer-based deep neural network model for SSVEP classification. arXiv preprint arXiv:2210.04172, 2022.
  • Li X, Wei W, Qiu S, et al. TFF-former: temporal-Frequency fusion transformer for zero-training decoding of two BCI tasks. Proceedings of the 30th ACM International Conference on Multimedia. Lisboa, Portugal, 10-14 October 2022. pp. 51–59.
  • Gao Z, Sun X, Liu M, et al. Attention-based parallel multiscale convolutional neural network for visual evoked potentials EEG classification. IEEE J. Biomed. Health Inform. 2021;25(8):2887–2894.
  • Guney OB, Oblokulov M, Ozkan H. A deep neural network for ssvep-based brain-computer interfaces. IEEE Trans. Biomed. Eng. 2022;69(2):932–944.
  • Rostami E, Ghassemi F, Tabanfar Z. Improving the classification of real-world SSVEP data in brain-computer interface speller systems using deep convolutional neural networks. Front. Biomed. Technol. 2022;9(4):248–254.
  • Guney OB, Ozkan H. Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training. arXiv preprint arXiv:2209015112022.
  • Rostami E, Ghassemi F, Tabanfar Z. Transfer learning assisted PodNet for stimulation frequency detection in steady state visually evoked potential-based BCI spellers. Brain-Computer Interfaces. 2022;10(1):1–12.
  • Bassi PRAS, Rampazzo W, Attux R. Transfer learning and SpecAugment applied to SSVEP based BCI classification. Biomed Signal Process Control. 2021;67:102542.
  • de Paula PO, da S, Costa TB, et al. Classification of image encoded SSVEP-based EEG signals using convolutional neural networks. Expert Syst Appl. 2022;214:119096.
  • Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Advances in neural information processing systems. 2014; 27: 2672–2680.
  • Hartmann KG, Schirrmeister RT, Ball T. EEG-GAN: generative adversarial networks for electroencephalograhic (EEG) brain signals. arXiv preprint arXiv:1806.01875, 2018.
  • Aznan NKN, Atapour-Abarghouei A, Bonner S, et al. Simulating brain signals: creating synthetic eeg data via neural-based generative models for improved ssvep classification. 2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary, 14-19 July 2019. pp. 1–8.
  • Aznan NKN, Atapour-Abarghouei A, Bonner S, et al. Leveraging synthetic subject invariant EEG signals for zero calibration BCI. 2020 25th international conference on pattern recognition (ICPR). Milan, Italy, 10-15 January 2021. pp. 10418–10425.
  • Kwon J, Im CH. Novel signal-to-signal translation method based on StarGAN to generate artificial EEG for SSVEP-based brain-computer interfaces. Expert Syst Appl. 2022;203:117574.
  • Yu Q, Yan R, Tang H, et al. A spiking neural network system for robust sequence recognition. IEEE Trans. Neural Netw. Learning Syst. 2016;27(3):621–635.
  • Deng L, Wang X, Jiang F, et al. EEG-based emotion recognition via capsule network with channel-wise attention and LSTM models. CCF Trans. Pervasive Comp. Interact. 2021;3(4):425–435.
  • Müller-Putz GR, Scherer R, Brauneis C, et al. Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J. Neural Eng. 2005;2(4):123–130.
  • Sevilla J, Heim L, Ho A, et al. Compute trends across three eras of machine learning. arXiv preprint arXiv:220205924, 2022.
  • Gao W, Yu T, Yu JG, et al. Learning invariant patterns based on a convolutional neural network and big electroencephalography data for subject-independent P300 brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 2021;29:1047–1057.
  • Jeon E, Ko W, Suk HI. Domain adaptation with source selection for motor-imagery based BCI. 2019 7th international winter conference on brain-computer interface (BCI). Gangwon, Korea (South), 18-20 February 2019. pp. 1–4.
  • Tang X, Zhang X. Conditional adversarial domain adaptation neural network for motor imagery EEG decoding. Entropy. 2020;22(1):96.
  • Krana M, Farmaki C, Pediaditis M, et al. SSVEP based wheelchair navigation in outdoor environments. 2021 43rd annual international conference of the IEEE engineering in medicine & biology society (EMBC). Mexico, 01-05 November 2021. pp. 6424–6427.
  • Zhang S, Ma K, Yin Y, et al. A personalized compression method for steady-state visual evoked potential EEG signals. Information. 2022;13(4):186.
  • Du Y, Liu J. IENet: a robust convolutional neural network for EEG based brain-computer interfaces. J. Neural Eng. 2022;19(3):036031.
  • Yan Z, Guo Y, Zhang C. Deep defense: training dnns with improved adversarial robustness. In Advances in Neural Information Processing Systems, pp. 419–428, 2018.
  • Jia S, Yin B, Yao T, et al. Adv-Attribute: inconspicuous and Transferable Adversarial Attack on Face Recognition. arXiv preprint arXiv:2210.06871 2022.
  • Bian R, Meng L, Wu D. SSVEP-based brain-computer interfaces are vulnerable to square wave attacks. Sci China Inf Sci. 2022;65(4):1–13.
  • Zhang X, Xu G, Mou X, et al. A convolutional neural network for the detection of asynchronous steady state motion visual evoked potential. IEEE Trans Neural Syst Rehabil Eng. 2019;27(6):1303–1311.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.