{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T17:59:33Z","timestamp":1781632773068,"version":"3.54.5"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s00521-024-10207-0","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T06:02:37Z","timestamp":1722492157000},"page":"18919-18934","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["KDTL: knowledge-distilled transfer learning framework for diagnosing mental disorders using EEG spectrograms"],"prefix":"10.1007","volume":"36","author":[{"given":"Shreyash","family":"Singh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harshit","family":"Jadli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6320-2203","authenticated-orcid":false,"given":"R.","family":"Padma Priya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V. B.","family":"Surya Prasath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"10207_CR1","doi-asserted-by":"crossref","first-page":"40144","DOI":"10.1109\/ACCESS.2019.2904400","volume":"7","author":"H Ullah","year":"2019","unstructured":"Ullah H et al (2019) Internal emotion classification using EEG signal with sparse discriminative ensemble. IEEE Access 7:40144\u201340153","journal-title":"IEEE Access"},{"key":"10207_CR2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00521-024-09550-z","volume":"36","author":"G Manasa","year":"2024","unstructured":"Manasa G et al (2024) EEG signal-based classification of mental tasks using a one-dimensional ConvResT model. Neural Comput Appl 36:1\u201320","journal-title":"Neural Comput Appl"},{"issue":"28","key":"10207_CR3","doi-asserted-by":"crossref","first-page":"20605","DOI":"10.1007\/s00521-023-08832-2","volume":"35","author":"S Shanmugam","year":"2023","unstructured":"Shanmugam S, Dharmar S (2023) A CNN-LSTM hybrid network for automatic seizure detection in EEG signals. Neural Comput Appl 35(28):20605\u201320617","journal-title":"Neural Comput Appl"},{"issue":"8","key":"10207_CR4","doi-asserted-by":"crossref","first-page":"3903","DOI":"10.1007\/s00521-023-09289-z","volume":"36","author":"Z Gao","year":"2024","unstructured":"Gao Z et al (2024) FAformer: parallel Fourier-attention architectures benefits EEG-based affective computing with enhanced spatial information. Neural Comput Appl 36(8):3903\u20133919","journal-title":"Neural Comput Appl"},{"key":"10207_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00521-024-09809-5","volume":"36","author":"Y Badr","year":"2024","unstructured":"Badr Y et al (2024) A review on evaluating mental stress by deep learning using EEG signals. Neural Comput Appl 36:1\u201326","journal-title":"Neural Comput Appl"},{"issue":"9","key":"10207_CR6","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1109\/TBME.2017.2650259","volume":"64","author":"A Bhattacharyya","year":"2017","unstructured":"Bhattacharyya A, Pachori RB (2017) A multivariate approach for patient-specific EEG seizure detection using empirical wavelet transform. IEEE Trans Biomed Eng 64(9):2003\u20132015","journal-title":"IEEE Trans Biomed Eng"},{"key":"10207_CR7","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.bspc.2016.05.004","volume":"29","author":"AB Das","year":"2016","unstructured":"Das AB, Bhuiyan MIH (2016) Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomed Signal Process Control 29:11\u201321","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"10207_CR8","first-page":"2088","volume":"9","author":"FP George","year":"2019","unstructured":"George FP et al (2019) Recognition of emotional states using EEG signals based on time-frequency analysis and SVM classifier. Int J Electr Comput Eng 9(2):2088\u20138708","journal-title":"Int J Electr Comput Eng"},{"issue":"4","key":"10207_CR9","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1109\/JBHI.2021.3110267","volume":"26","author":"J Zheng","year":"2021","unstructured":"Zheng J et al (2021) Time-frequency analysis of scalp EEG with Hilbert-Huang transform and deep learning. IEEE J Biomed Health Inform 26(4):1549\u20131559","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"10207_CR10","doi-asserted-by":"crossref","first-page":"016003","DOI":"10.1088\/1741-2560\/14\/1\/016003","volume":"14","author":"YR Tabar","year":"2016","unstructured":"Tabar YR, Ugur H (2016) A novel deep learning approach for classification of EEG motor imagery signals. J Neural Eng 14(1):016003","journal-title":"J Neural Eng"},{"issue":"6","key":"10207_CR11","doi-asserted-by":"crossref","first-page":"3078","DOI":"10.1109\/JSEN.2019.2956072","volume":"20","author":"S Madhavan","year":"2019","unstructured":"Madhavan S, Tripathy RK, Pachori RB (2019) Time-frequency domain deep convolutional neural network for the classification of focal and non-focal EEG signals. IEEE Sens J 20(6):3078\u20133086","journal-title":"IEEE Sens J"},{"issue":"25","key":"10207_CR12","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1049\/el.2020.2646","volume":"56","author":"MNA Tawhid","year":"2020","unstructured":"Tawhid MNA, Siuly S, Hua W (2020) Diagnosis of autism spectrum disorder from EEG using a time\u2013frequency spectrogram image-based approach. Electron Lett 56(25):1372\u20131375","journal-title":"Electron Lett"},{"issue":"11","key":"10207_CR13","first-page":"4608","volume":"69","author":"C Zhang","year":"2022","unstructured":"Zhang C et al (2022) multichannel multidomain-based knowledge distillation algorithm for sleep staging with single-channel EEG. IEEE Trans Syst II Exp Br 69(11):4608\u20134612","journal-title":"IEEE Trans Syst II Exp Br"},{"issue":"8","key":"10207_CR14","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.3390\/biom11081093","volume":"11","author":"NA Khan","year":"2021","unstructured":"Khan NA et al (2021) A novel knowledge distillation-based feature selection for the classification of ADHD. Biomolecules 11(8):1093","journal-title":"Biomolecules"},{"issue":"6","key":"10207_CR15","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou J et al (2021) Knowledge distillation: a survey. Int J Comput Vision 129(6):1789\u20131819","journal-title":"Int J Comput Vision"},{"key":"10207_CR16","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.patrec.2023.05.011","volume":"171","author":"G Zhang","year":"2021","unstructured":"Zhang G, Etemad A (2021) Distilling EEG representations via capsules for affective computing. Pattern Recognit Lett 171:99\u2013105","journal-title":"Pattern Recognit Lett"},{"key":"10207_CR17","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.neunet.2019.12.006","volume":"123","author":"C Ieracitano","year":"2020","unstructured":"Ieracitano C et al (2020) A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Netw 123:176\u2013190","journal-title":"Neural Netw"},{"issue":"7553","key":"10207_CR18","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"10207_CR19","doi-asserted-by":"crossref","unstructured":"He, K., et al. (2016) \"Deep residual learning for image recognition.\" Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.90"},{"key":"10207_CR20","doi-asserted-by":"crossref","unstructured":"Liu Z, et al. (2022) A convnet for the 2020s. Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"10207_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/JRFID.2022.3206841","author":"Y Tao","year":"2022","unstructured":"Tao Y, Chang F, Huang Y, Ma L, Xie L, Su H (2022) Cotton disease detection based on ConvNeXt and attention mechanisms. IEEE J Radio Freq Identif. https:\/\/doi.org\/10.1109\/JRFID.2022.3206841","journal-title":"IEEE J Radio Freq Identif"},{"key":"10207_CR22","doi-asserted-by":"crossref","first-page":"105632","DOI":"10.1016\/j.engappai.2022.105632","volume":"117","author":"S Fan","year":"2023","unstructured":"Fan S et al (2023) LACN: A lightweight attention-guided ConvNeXt network for low-light image enhancement. Eng Appl Artif Intell 117:105632","journal-title":"Eng Appl Artif Intell"},{"key":"10207_CR23","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu J et al (2018) Recent advances in convolutional neural networks. Pattern Recognit 77:354\u2013377","journal-title":"Pattern Recognit"},{"key":"10207_CR24","doi-asserted-by":"crossref","unstructured":"He K, et al. (2017) Mask R-CNN. Proceedings of the IEEE international conference on computer vision","DOI":"10.1109\/ICCV.2017.322"},{"key":"10207_CR25","doi-asserted-by":"crossref","first-page":"160749","DOI":"10.1109\/ACCESS.2020.3020802","volume":"8","author":"TKK Ho","year":"2020","unstructured":"Ho TKK, Gwak J (2020) Utilizing knowledge distillation in deep learning for classification of chest X-ray abnormalities. IEEE Access 8:160749\u2013160761","journal-title":"IEEE Access"},{"issue":"1","key":"10207_CR26","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.bbe.2017.08.006","volume":"38","author":"S Ibrahim","year":"2018","unstructured":"Ibrahim S, Djemal R, Alsuwailem A (2018) Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis. Biocybern Biomed Eng 38(1):16\u201326","journal-title":"Biocybern Biomed Eng"},{"key":"10207_CR27","doi-asserted-by":"crossref","first-page":"106273","DOI":"10.1016\/j.compbiomed.2022.106273","volume":"151","author":"Mi Jia","year":"2022","unstructured":"Jia Mi et al (2022) KDE-GAN: A multimodal medical image-fusion model based on knowledge distillation and explainable AI modules. Comput Biol Med 151:106273","journal-title":"Comput Biol Med"},{"key":"10207_CR28","first-page":"1","volume":"27","author":"C Bi","year":"2020","unstructured":"Bi C et al (2020) MobileNet based apple leaf diseases identification. Mob Netw Appl 27:1\u20139","journal-title":"Mob Netw Appl"},{"key":"10207_CR29","first-page":"1","volume":"2020","author":"W Wang","year":"2020","unstructured":"Wang W et al (2020) A novel image classification approach via dense-MobileNet models. Mob Inform Syst. 2020:1","journal-title":"Mob Inform Syst."},{"key":"10207_CR30","unstructured":"Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y (2013) OverFeat: integrated recognition, localization and detection using convolutional networks. arXiv:1312,6229."},{"key":"10207_CR31","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"10207_CR32","unstructured":"Howard AG, et al. (2017) MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"issue":"10","key":"10207_CR33","doi-asserted-by":"crossref","first-page":"11006","DOI":"10.1109\/TVT.2022.3184994","volume":"71","author":"J Yang","year":"2022","unstructured":"Yang J et al (2022) MobileNet and knowledge distillation-based automatic scenario recognition method in vehicle-to-vehicle systems. IEEE Trans Veh Technol 71(10):11006\u201311016","journal-title":"IEEE Trans Veh Technol"},{"key":"10207_CR34","doi-asserted-by":"crossref","unstructured":"Chiu YC, et al. (2020) Mobilenet-SSDv2: An improved object detection model for embedded systems. International conference on system science and engineering (ICSSE). IEEE","DOI":"10.1109\/ICSSE50014.2020.9219319"},{"key":"10207_CR35","doi-asserted-by":"crossref","unstructured":"Sun Y, Zhang J, Chaoyue H (2021) A flower recognition system based on MobileNet for smart agriculture. IEEE 3rd international conference on frontiers technology of information and computer (ICFTIC). IEEE","DOI":"10.1109\/ICFTIC54370.2021.9647097"},{"key":"10207_CR36","doi-asserted-by":"crossref","unstructured":"Yin H, et al. (2020) Dreaming to distill: Data-free knowledge transfer via deepinversion. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR42600.2020.00874"},{"key":"10207_CR37","unstructured":"Malinin A, Mlodozeniec B, Gales M (2019) Ensemble distribution distillation. arXiv preprint arXiv:1905.00076"},{"key":"10207_CR38","doi-asserted-by":"crossref","unstructured":"Cho JH, Hariharan B (2019) On the efficacy of knowledge distillation. Proceedings of the IEEE\/CVF international conference on computer vision","DOI":"10.1109\/ICCV.2019.00489"},{"key":"10207_CR39","doi-asserted-by":"crossref","unstructured":"Heo B, et al. (2019) A comprehensive overhaul of feature distillation. Proceedings of the IEEE\/CVF international conference on computer vision","DOI":"10.1109\/ICCV.2019.00201"},{"key":"10207_CR40","unstructured":"Gao M et al. (2020) Residual knowledge distillation. arXiv preprint arXiv:2002.09168"},{"key":"10207_CR41","unstructured":"Kulkarni A et al. (2019) Data efficient stagewise knowledge distillation. arXiv preprint arXiv:1911.06786"},{"issue":"01","key":"10207_CR42","first-page":"4886","volume":"33","author":"Z Shen","year":"2019","unstructured":"Shen Z, He Z, Xue X (2019) Meal: Multi-model ensemble via adversarial learning. Proc AAAI Conf Artif Intell. 33(01):4886","journal-title":"Proc AAAI Conf Artif Intell."},{"issue":"05","key":"10207_CR43","first-page":"7350","volume":"34","author":"G Aguilar","year":"2020","unstructured":"Aguilar G et al (2020) Knowledge distillation from internal representations. Proc AAAI Conf Artif Intell. 34(05):7350","journal-title":"Proc AAAI Conf Artif Intell."},{"issue":"23","key":"10207_CR44","doi-asserted-by":"crossref","first-page":"9272","DOI":"10.3390\/s22239272","volume":"22","author":"Q Wang","year":"2022","unstructured":"Wang Q et al (2022) Multi-layer graph attention network for sleep stage classification based on EEG. Sensors 22(23):9272","journal-title":"Sensors"},{"key":"10207_CR45","doi-asserted-by":"crossref","first-page":"150252","DOI":"10.1109\/ACCESS.2021.3126065","volume":"9","author":"M Yazid","year":"2021","unstructured":"Yazid M et al (2021) Simple detection of epilepsy from EEG signal using local binary pattern transition histogram. IEEE Access 9:150252\u2013150267","journal-title":"IEEE Access"},{"issue":"5","key":"10207_CR46","doi-asserted-by":"crossref","first-page":"5287","DOI":"10.3233\/JIFS-189851","volume":"41","author":"R Agrawal","year":"2021","unstructured":"Agrawal R, Bajaj P (2021) Comparative classification techniques for identification of brain states using TQWT decomposition. J Intell Fuzzy Syst 41(5):5287\u20135297","journal-title":"J Intell Fuzzy Syst"},{"key":"10207_CR47","doi-asserted-by":"crossref","unstructured":"Li Y, et al. (2022). Automatic sleep stage classification based on two-channel EOG and one-channel EMG. Physiological Measurement","DOI":"10.21203\/rs.3.rs-491468\/v1"},{"issue":"2","key":"10207_CR48","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.irbm.2018.12.002","volume":"40","author":"G Chandel","year":"2019","unstructured":"Chandel G et al (2019) Detection of seizure event and its onset\/offset using orthonormal triadic wavelet based features. IRBM 40(2):103\u2013112","journal-title":"IRBM"},{"key":"10207_CR49","doi-asserted-by":"crossref","first-page":"103281","DOI":"10.1016\/j.bspc.2021.103281","volume":"72","author":"SK Rout","year":"2022","unstructured":"Rout SK et al (2022) An efficient epileptic seizure classification system using empirical wavelet transform and multi-fuse reduced deep convolutional neural network with digital implementation. Biomed Signal Process Control 72:103281","journal-title":"Biomed Signal Process Control"},{"key":"10207_CR50","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2021.107154","volume":"92","author":"A Kukker","year":"2021","unstructured":"Kukker A, Sharma R (2021) A genetic algorithm assisted fuzzy Q-learning epileptic seizure classifier. Comput Electr Eng 92:107154","journal-title":"Comput Electr Eng"},{"key":"10207_CR51","doi-asserted-by":"crossref","first-page":"101787","DOI":"10.1016\/j.bspc.2019.101787","volume":"57","author":"SK Rout","year":"2020","unstructured":"Rout SK, Biswal PK (2020) An efficient error-minimized random vector functional link network for epileptic seizure classification using VMD. Biomed Signal Process Control 57:101787","journal-title":"Biomed Signal Process Control"},{"issue":"5","key":"10207_CR52","doi-asserted-by":"crossref","first-page":"622","DOI":"10.3390\/life12050622","volume":"12","author":"M Li","year":"2022","unstructured":"Li M, Chen H, Cheng Z (2022) An attention-guided spatiotemporal graph convolutional network for sleep stage classification. Life 12(5):622","journal-title":"Life"},{"issue":"9","key":"10207_CR53","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.3390\/diagnostics11091571","volume":"11","author":"R Jayaraj","year":"2021","unstructured":"Jayaraj R, Mohan J (2021) Classification of sleep apnea based on sub-band decomposition of EEG signals. Diagnostics 11(9):1571","journal-title":"Diagnostics"},{"key":"10207_CR54","doi-asserted-by":"crossref","first-page":"103645","DOI":"10.1016\/j.bspc.2022.103645","volume":"76","author":"G Kaushik","year":"2022","unstructured":"Kaushik G et al (2022) EEG signal based seizure detection focused on Hjorth parameters from tunable-Q wavelet sub-bands. Biomed Signal Process Control 76:103645","journal-title":"Biomed Signal Process Control"},{"key":"10207_CR55","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.neucom.2021.02.052","volume":"444","author":"M Woodbright","year":"2021","unstructured":"Woodbright M, Verma B, Haidar A (2021) Autonomous deep feature extraction based method for epileptic EEG brain seizure classification. Neurocomputing 444:30\u201337","journal-title":"Neurocomputing"},{"key":"10207_CR56","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1109\/LSP.2021.3130826","volume":"29","author":"J Ye","year":"2021","unstructured":"Ye J et al (2021) CoSleep: A multi-view representation learning framework for self-supervised learning of sleep stage classification. IEEE Signal Process Lett 29:189\u2013193","journal-title":"IEEE Signal Process Lett"},{"key":"10207_CR57","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s00521-016-2646-4","volume":"29","author":"A Bhattacharyya","year":"2018","unstructured":"Bhattacharyya A et al (2018) A novel approach for automated detection of focal EEG signals using empirical wavelet transform. Neural Comput Appl 29:47\u201357","journal-title":"Neural Comput Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10207-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10207-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10207-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T16:43:36Z","timestamp":1726764216000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10207-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,1]]},"references-count":57,"journal-issue":{"issue":"30","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["10207"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10207-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,1]]},"assertion":[{"value":"3 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}