{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T17:03:29Z","timestamp":1785603809860,"version":"3.56.0"},"reference-count":83,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007569","name":"Carl-Zeiss-Stiftung","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007569","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroinform."],"abstract":"<jats:p>Analyzing time series data like EEG or MEG is challenging due to noisy, high-dimensional, and patient-specific signals. Deep learning methods have been demonstrated to be superior in analyzing time series data compared to shallow learning methods which utilize handcrafted and often subjective features. Especially, recurrent deep neural networks (RNN) are considered suitable to analyze such continuous data. However, previous studies show that they are computationally expensive and difficult to train. In contrast, feed-forward networks (FFN) have previously mostly been considered in combination with hand-crafted and problem-specific feature extractions, such as short time Fourier and discrete wavelet transform. A sought-after are easily applicable methods that efficiently analyze raw data to remove the need for problem-specific adaptations. In this work, we systematically compare RNN and FFN topologies as well as advanced architectural concepts on multiple datasets with the same data preprocessing pipeline. We examine the behavior of those approaches to provide an update and guideline for researchers who deal with automated analysis of EEG time series data. To ensure that the results are meaningful, it is important to compare the presented approaches while keeping the same experimental setup, which to our knowledge was never done before. This paper is a first step toward a fairer comparison of different methodologies with EEG time series data. Our results indicate that a recurrent LSTM architecture with attention performs best on less complex tasks, while the temporal convolutional network (TCN) outperforms all the recurrent architectures on the most complex dataset yielding a 8.61% accuracy improvement. In general, we found the attention mechanism to substantially improve classification results of RNNs. Toward a light-weight and online learning-ready approach, we found extreme learning machines (ELM) to yield comparable results for the less complex tasks.<\/jats:p>","DOI":"10.3389\/fninf.2023.1067095","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T07:44:06Z","timestamp":1677138246000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":46,"title":["A systematic comparison of deep learning methods for EEG time series analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Dominik","family":"Walther","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Viehweg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Haueisen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick","family":"M\u00e4der","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/ICMEW46912.2020.9106021","article-title":"Multi-cnn feature fusion for efficient eeg classification","volume-title":"2020 IEEE International Conference on Multimedia &Expo Workshops (ICMEW)","author":"Amin","year":"2020"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1409.0473","article-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau","year":"2014","journal-title":"arXiv preprint"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1803.01271","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018","journal-title":"arXiv preprint"},{"key":"B4","unstructured":"BCI IV Dataset2008"},{"key":"B5","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.neunet.2015.07.005","article-title":"Learning to decode human emotions with echo state networks","volume":"78","author":"Bozhkov","year":"2016","journal-title":"Neural Networks"},{"key":"B6","doi-asserted-by":"crossref","first-page":"9598","DOI":"10.23919\/ChiCC.2018.8484033","article-title":"The motor imagination eeg recognition combined with convolution neural network and gated recurrent unit","volume-title":"2018 37th Chinese Control Conference (CCC)","author":"Cai","year":"2018"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.31223\/OSF.IO\/FBXNS","article-title":"Data-driven prediction of a multi-scale lorenz 96 chaotic system using a hierarchy of deep learning methods: reservoir computing, ann, and rnn-lstm","author":"Chattopadhyay","year":"2019","journal-title":"arXiv preprint"},{"key":"B8","doi-asserted-by":"publisher","first-page":"118530","DOI":"10.1109\/ACCESS.2019.2936817","article-title":"A hierarchical bidirectional gru model with attention for eeg-based emotion classification","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1053","article-title":"Long short-term memory-networks for machine reading","author":"Cheng","year":"2016","journal-title":"arXiv preprint"},{"key":"B10","doi-asserted-by":"publisher","first-page":"309","DOI":"10.4258\/hir.2018.24.4.309","article-title":"Arousal and valence classification model based on long short-term memory and deap data for mental healthcare management","volume":"24","author":"Choi","year":"2018","journal-title":"Healthc Inform. Res"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1412.3555","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"Chung","year":"2014","journal-title":"arXiv preprint"},{"key":"B12","doi-asserted-by":"publisher","first-page":"031001","DOI":"10.1088\/1741-2552\/ab0ab5","article-title":"Deep learning for electroencephalogram (eeg) classification tasks: a review","volume":"16","author":"Craik","year":"2019","journal-title":"J. Neural Eng"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1285","article-title":"Transformer-xl: attentive language models beyond a fixed-length context","author":"Dai","year":"2019","journal-title":"arXiv preprint"},{"key":"B14","first-page":"1690","article-title":"A deep learning MI-EEG classification model for bcis","volume-title":"2018 26th European Signal Processing Conference","author":"Dose","year":"2018"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1528","DOI":"10.1109\/TAFFC.2020.3013711","article-title":"An efficient lstm network for emotion recognition from multichannel eeg signals","volume":"13","author":"Du","year":"2020","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B16","volume-title":"Echo State Networks for Modeling and Classification of EEG Signals in Mental-Task Brain-Computer Interfaces","author":"Forney","year":"2015"},{"key":"B17","doi-asserted-by":"publisher","first-page":"2755","DOI":"10.1109\/TNNLS.2018.2886414","article-title":"EEG-based spatio-temporal convolutional neural network for driver fatigue evaluation","volume":"30","author":"Gao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"},{"key":"B18","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1109\/TCDS.2021.3079712","article-title":"Deep learning in EEG: advance of the last ten-year critical period","volume":"14","author":"Gong","year":"2021","journal-title":"IEEE Trans. Cogn. Dev. Syst"},{"key":"B19","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1109\/TASSP.1984.1164317","article-title":"Signal estimation from modified short-time fourier transform","volume":"32","author":"Griffin","year":"1984","journal-title":"IEEE Trans. Acoust"},{"key":"B20","doi-asserted-by":"publisher","first-page":"2552","DOI":"10.1542\/peds.2006-2519","article-title":"Artifacts on electroencephalograms may influence the amplitude-integrated eeg classification: a qualitative analysis in neonatal encephalopathy","volume":"118","author":"Hagmann","year":"2006","journal-title":"Pediatrics"},{"key":"B21","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1300\/J184v10n04_04","article-title":"What is neurofeedback?","volume":"10","author":"Hammond","year":"2007","journal-title":"J. Neurother"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"B23","doi-asserted-by":"publisher","first-page":"3094","DOI":"10.1109\/TNNLS.2021.3050422","article-title":"Synaptic scaling-an artificial neural network regularization inspired by nature","volume":"33","author":"Hofmann","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"},{"key":"B24","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1109\/IJCNN.2004.1380068","article-title":"Extreme learning machine: a new learning scheme of feedforward neural networks","volume-title":"2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), Vol. 2","author":"Huang","year":"2004"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1508.01991","article-title":"Bidirectional lstm-crf models for sequence tagging","author":"Huang","year":"2015","journal-title":"arXiv preprint"},{"key":"B26","doi-asserted-by":"crossref","first-page":"2958","DOI":"10.1109\/SMC42975.2020.9283028","article-title":"EEG-tcnet: an accurate temporal convolutional network for embedded motor-imagery brain-machine interfaces","volume-title":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","author":"Ingolfsson","year":"2020"},{"key":"B27","first-page":"1","article-title":"Sleep stage classification based on EEG, EOG, and CNN-gru deep learning model","volume-title":"2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)","author":"Isuru Niroshana","year":"2019"},{"key":"B28","volume-title":"The echo state approach to analysing and training recurrent neural networks-with an erratum note","author":"Jaeger","year":"2001"},{"key":"B29","doi-asserted-by":"publisher","first-page":"348","DOI":"10.3390\/brainsci9120348","article-title":"Classification of drowsiness levels based on a deep spatio-temporal convolutional bidirectional lstm network using electroencephalography signals","volume":"9","author":"Jeong","year":"2019","journal-title":"Brain Sci"},{"key":"B30","doi-asserted-by":"publisher","first-page":"2634","DOI":"10.1109\/JSEN.2018.2885582","article-title":"Eeg-based age and gender prediction using deep blstm-lstm network model","volume":"19","author":"Kaushik","year":"2018","journal-title":"IEEE Sens. J"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1910.09719","article-title":"Spatiotemporal emotion recognition using deep CNN based on eeg during music listening","author":"Keelawat","year":"2019","journal-title":"arXiv preprint"},{"key":"B32","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"Deap: a database for emotion analysis; using physiological signals","volume":"3","author":"Koelstra","year":"2011","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B33","unstructured":"Autonomio Talos\n            KotilaM.\n          Autonomio2019"},{"key":"B34","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1109\/BIBM.2016.7822545","article-title":"Emotion recognition from multi-channel eeg data through convolutional recurrent neural network","volume-title":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Li","year":"2016"},{"key":"B35","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1142\/S0129065706000482","article-title":"Classification of mental tasks from eeg signals using extreme learning machine","volume":"16","author":"Liang","year":"2006","journal-title":"Int. J. Neural Syst"},{"key":"B36","first-page":"5124","article-title":"Noisy recurrent neural networks","volume":"34","author":"Lim","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst"},{"key":"B37","first-page":"001","article-title":"Classification of eeg signal by stft-cnn framework: identification of right-\/left-hand motor imagination in BCI systems","volume-title":"The 7th International Conference on Computer Engineering and Networks, Vol. 299","author":"Lu","year":"2017"},{"key":"B38","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1109\/TCYB.2016.2533545","article-title":"Multilayered echo state machine: a novel architecture and algorithm","volume":"47","author":"Malik","year":"2016","journal-title":"IEEE Trans. Cybern"},{"key":"B39","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1515\/bmt-2020-0295","article-title":"Emotion recognition using time-frequency ridges of EEG signals based on multivariate synchrosqueezing transform","volume":"66","author":"Mert","year":"2021","journal-title":"Biomed. Eng"},{"key":"B40","first-page":"762","article-title":"Training feedforward neural networks using genetic algorithms","volume":"89","author":"Montana","year":"1989","journal-title":"IJCAI"},{"key":"B41","doi-asserted-by":"publisher","first-page":"261","DOI":"10.3389\/fnins.2020.00261","article-title":"Coupled cp decomposition of simultaneous MEG-EEG signals for differentiating oscillators during photic driving","volume":"14","author":"Naskovska","year":"2020","journal-title":"Front. Neurosci"},{"key":"B42","first-page":"443","article-title":"Deep learning for emotion recognition on small datasets using transfer learning","author":"Ng","year":"2015","journal-title":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction"},{"key":"B43","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1145\/3107411.3107513","article-title":"Confused or not confused?: disentangling brain activity from eeg data using bidirectional lstm recurrent neural networks","author":"Ni","year":"2017","journal-title":"Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics"},{"key":"B44","doi-asserted-by":"publisher","first-page":"61401","DOI":"10.1109\/ACCESS.2018.2876710","article-title":"Favorite video classification based on multimodal bidirectional lstm","volume":"6","author":"Ogawa","year":"2018","journal-title":"IEEE Access"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1609.03499","article-title":"Wavenet: a generative model for raw audio","author":"Oord","year":"2016","journal-title":"arXiv preprint"},{"key":"B46","doi-asserted-by":"publisher","first-page":"045106","DOI":"10.1063\/5.0087977","article-title":"Direct data-driven forecast of local turbulent heat flux in rayleigh-b\u00e9nard convection","volume":"34","author":"Pandey","year":"2022","journal-title":"Phys. Fluids"},{"key":"B47","unstructured":"Physionet MI Dataset2009"},{"key":"B48","doi-asserted-by":"publisher","DOI":"10.2478\/pralin-2018-0002","article-title":"Training tips for the transformer model","author":"Popel","year":"2018","journal-title":"arXiv preprint"},{"key":"B49","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1109\/TETCI.2018.2858761","article-title":"Hjb-equation-based optimal learning scheme for neural networks with applications in brain-computer interface","volume":"4","author":"Reddy","year":"2018","journal-title":"IEEE Trans. Emerg. Topics Comput. Intell"},{"key":"B50","first-page":"1","article-title":"Learning representations by back-propagating errors","volume":"5","author":"Rumelhart","year":"1988","journal-title":"Cognit. Model"},{"key":"B51","doi-asserted-by":"publisher","first-page":"413","DOI":"10.3389\/fnhum.2016.00413","article-title":"Rod driven frequency entrainment and resonance phenomena","volume":"10","author":"Salchow","year":"2016","journal-title":"Front. Hum. Neurosci"},{"key":"B52","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1109\/TBME.2004.827072","article-title":"Bci2000: a general-purpose brain-computer interface (bci) system","volume":"51","author":"Schalk","year":"2004","journal-title":"IEEE Trans. Biomed. Eng"},{"key":"B53","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional recurrent neural networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Trans. Signal Process"},{"key":"B54","unstructured":"SEED Dataset2013"},{"key":"B55","first-page":"373","article-title":"Single-trial eeg rsvp classification using convolutional neural networks","volume-title":"Micro-and Nanotechnology Sensors, Systems, and Applications VIII","author":"Shamwell","year":"2016"},{"key":"B56","doi-asserted-by":"publisher","first-page":"2464","DOI":"10.1109\/78.157290","article-title":"The discrete wavelet transform: wedding the a trous and mallat algorithms","volume":"40","author":"Shensa","year":"1992","journal-title":"IEEE Trans. Signal Process"},{"key":"B57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2018.09.057","article-title":"Unsupervised eeg feature extraction based on echo state network","volume":"475","author":"Sun","year":"2019","journal-title":"Inf. Sci"},{"key":"B58","volume-title":"Training Recurrent Neural Networks","author":"Sutskever","year":"2013"},{"key":"B59","doi-asserted-by":"publisher","first-page":"016003","DOI":"10.1088\/1741-2560\/14\/1\/016003","article-title":"A novel deep learning approach for classification of eeg motor imagery signals","volume":"14","author":"Tabar","year":"2016","journal-title":"J. Neural Eng"},{"key":"B60","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/CYBER.2016.7574827","article-title":"Applying extreme learning machine to classification of EEG BCI","volume-title":"2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)","author":"Tan","year":"2016"},{"key":"B61","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.ijleo.2016.10.117","article-title":"Single-trial eeg classification of motor imagery using deep convolutional neural networks","volume":"130","author":"Tang","year":"2017","journal-title":"Optik"},{"key":"B62","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neunet.2018.12.002","article-title":"Deep learning in spiking neural networks","volume":"111","author":"Tavanaei","year":"2019","journal-title":"Neural Networks"},{"key":"B63","unstructured":"Flipped classroom: effective teaching for time series forecasting\n            TeutschP.\n            M\u00e4derP.\n          Trans. Mach. Learn. Res2022"},{"key":"B64","first-page":"178","article-title":"Learning robust features using deep learning for automatic seizure detection","volume-title":"Machine Learning for Healthcare Conference","author":"Thodoroff","year":"2016"},{"key":"B65","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/TITB.2009.2017939","article-title":"Epileptic seizure detection in eegs using time-frequency analysis","volume":"13","author":"Tzallas","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed"},{"key":"B66","doi-asserted-by":"publisher","first-page":"031001","DOI":"10.1088\/1741-2560\/12\/3\/031001","article-title":"EEG artifact removal state-of-the-art and guidelines","volume":"12","author":"Urig\u00fcen","year":"2015","journal-title":"J. Neural Eng"},{"key":"B67","doi-asserted-by":"publisher","first-page":"125778","DOI":"10.1109\/ACCESS.2021.3105917","article-title":"Deep learning algorithms in eeg signal decoding application: a review","volume":"9","author":"Vallabhaneni","year":"2021","journal-title":"IEEE Access"},{"key":"B68","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"B69","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1016\/j.neunet.2007.04.003","article-title":"An experimental unification of reservoir computing methods","volume":"20","author":"Verstraeten","year":"2007","journal-title":"Neural Networks"},{"key":"B70","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neucom.2022.11.044","article-title":"Parameterizing echo state networks for multi-step time series prediction","volume":"522","author":"Viehweg","year":"2022","journal-title":"Neurocomputing"},{"key":"B71","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.neunet.2020.02.016","article-title":"Backpropagation algorithms and reservoir computing in recurrent neural networks for the forecasting of complex spatiotemporal dynamics","volume":"126","author":"Vlachas","year":"2020","journal-title":"Neural Networks"},{"key":"B72","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1016\/j.procs.2020.06.117","article-title":"Eeg-based emotion classification based on bidirectional long short-term memory network","volume":"174","author":"Yang","year":"2020","journal-title":"Procedia Comput. Sci"},{"key":"B73","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1109\/DDCLS.2019.8908839","article-title":"EEG-based emotion recognition using temporal convolutional network","volume-title":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","author":"Yang","year":"2019"},{"key":"B74","doi-asserted-by":"publisher","first-page":"850945","DOI":"10.3389\/fnins.2022.850945","article-title":"Sam: A unified self-adaptive multicompartmental spiking neuron model for learning with working memory","volume":"16","author":"Yang","year":"","journal-title":"Front. Neurosci"},{"key":"B75","doi-asserted-by":"publisher","first-page":"850932","DOI":"10.3389\/fnins.2022.850932","article-title":"Heterogeneous ensemble-based spike-driven few-shot online learning","volume":"16","author":"Yang","year":"","journal-title":"Front. Neurosci"},{"key":"B76","doi-asserted-by":"publisher","first-page":"455","DOI":"10.3390\/e24040455","article-title":"Robust spike-based continual meta-learning improved by restricted minimum error entropy criterion","volume":"24","author":"Yang","year":"","journal-title":"Entropy"},{"key":"B77","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1515\/bmt-2020-0229","article-title":"A novel signal to image transformation and feature level fusion for multimodal emotion recognition","volume":"66","author":"Yilmaz","year":"2021","journal-title":"Biomed. Tech"},{"key":"B78","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1702.01923","article-title":"Comparative study of CNN and rnn for natural language processing","author":"Yin","year":"2017","journal-title":"arXiv preprint"},{"key":"B79","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1109\/TBCAS.2019.2959160","article-title":"Robust real-time embedded emg recognition framework using temporal convolutional networks on a multicore iot processor","volume":"14","author":"Zanghieri","year":"2019","journal-title":"IEEE Trans. Biomed. Circ. Syst"},{"key":"B80","doi-asserted-by":"publisher","first-page":"3113","DOI":"10.1109\/JSEN.2019.2956998","article-title":"Classification of hand movements from eeg using a deep attention-based lstm network","volume":"20","author":"Zhang","year":"2019","journal-title":"IEEE Sens. J"},{"key":"B81","doi-asserted-by":"publisher","first-page":"814","DOI":"10.1109\/TNSRE.2019.2908955","article-title":"On the vulnerability of cnn classifiers in eeg-based bcis","volume":"27","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng"},{"key":"B82","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","article-title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","volume":"7","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Auton. Ment. Dev"},{"key":"B83","doi-asserted-by":"publisher","first-page":"17325","DOI":"10.1609\/aaai.v35i12.17325","article-title":"Informer: beyond efficient transformer for long sequence time-series forecasting","volume":"2021","author":"Zhou","year":"2021","journal-title":"Proc. AAAI"}],"container-title":["Frontiers in Neuroinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2023.1067095\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T07:44:29Z","timestamp":1677138269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fninf.2023.1067095\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,23]]},"references-count":83,"alternative-id":["10.3389\/fninf.2023.1067095"],"URL":"https:\/\/doi.org\/10.3389\/fninf.2023.1067095","relation":{},"ISSN":["1662-5196"],"issn-type":[{"value":"1662-5196","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,23]]},"article-number":"1067095"}}