{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T05:13:58Z","timestamp":1777958038056,"version":"3.51.4"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"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 &amp; Applic"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s00521-026-12086-z","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T04:45:27Z","timestamp":1777956327000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modified TSception for analyzing driver drowsiness and mental workload from EEG"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5883-3863","authenticated-orcid":false,"given":"Gourav","family":"Siddhad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anurag","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8532-0895","authenticated-orcid":false,"given":"Rajkumar","family":"Saini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5735-5254","authenticated-orcid":false,"given":"Partha Pratim","family":"Roy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,5]]},"reference":[{"key":"12086_CR1","doi-asserted-by":"publisher","first-page":"143116","DOI":"10.1109\/access.2023.3341419","volume":"11","author":"NS Amer","year":"2023","unstructured":"Amer NS, Belhaouari SB (2023) EEG signal processing for medical diagnosis, healthcare, and monitoring: a comprehensive review. IEEE Access 11:143116\u2013143142. https:\/\/doi.org\/10.1109\/access.2023.3341419","journal-title":"IEEE Access"},{"key":"12086_CR2","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2019.00325","volume":"10","author":"CM Michel","year":"2019","unstructured":"Michel CM, Brunet D (2019) EEG source imaging: a practical review of the analysis steps. Front Neurol 10:325. https:\/\/doi.org\/10.3389\/fneur.2019.00325","journal-title":"Front Neurol"},{"issue":"3","key":"12086_CR3","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab0ab5","volume":"16","author":"A Craik","year":"2019","unstructured":"Craik A, He Y, Contreras-Vidal JL (2019) Deep learning for electroencephalogram (EEG) classification tasks: a review. J Neural Eng 16(3):031001. https:\/\/doi.org\/10.1088\/1741-2552\/ab0ab5","journal-title":"J Neural Eng"},{"issue":"5","key":"12086_CR4","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab260c","volume":"16","author":"Y Roy","year":"2019","unstructured":"Roy Y, Banville H, Albuquerque I, Gramfort A, Falk TH, Faubert J (2019) Deep learning-based electroencephalography analysis: a systematic review. J Neural Eng 16(5):051001. https:\/\/doi.org\/10.1088\/1741-2552\/ab260c","journal-title":"J Neural Eng"},{"key":"12086_CR5","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.ymeth.2021.04.017","volume":"202","author":"J Cui","year":"2022","unstructured":"Cui J, Lan Z, Liu Y, Li R, Li F, Sourina O, M\u00fcller-Wittig W (2022) A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG. Methods 202:173\u2013184. https:\/\/doi.org\/10.1016\/j.ymeth.2021.04.017","journal-title":"Methods"},{"issue":"11","key":"12086_CR6","doi-asserted-by":"publisher","DOI":"10.3390\/s21113786","volume":"21","author":"I Stancin","year":"2021","unstructured":"Stancin I, Cifrek M, Jovic A (2021) A review of EEG signal features and their application in driver drowsiness detection systems. Sensors 21(11):3786. https:\/\/doi.org\/10.3390\/s21113786","journal-title":"Sensors"},{"issue":"13","key":"12086_CR7","doi-asserted-by":"publisher","first-page":"4764","DOI":"10.3390\/s22134764","volume":"22","author":"S Tarafder","year":"2022","unstructured":"Tarafder S, Badruddin N, Yahya N, Nasution AH (2022) Drowsiness detection using ocular indices from EEG signal. Sensors 22(13):4764. https:\/\/doi.org\/10.3390\/s22134764","journal-title":"Sensors"},{"issue":"1","key":"12086_CR8","doi-asserted-by":"publisher","DOI":"10.1038\/srep43933","volume":"7","author":"T Nguyen","year":"2017","unstructured":"Nguyen T, Ahn S, Jang H, Jun SC, Kim JG (2017) Utilization of a combined EEG\/NIRS system to predict driver drowsiness. Sci Rep 7(1):43933. https:\/\/doi.org\/10.1038\/srep43933","journal-title":"Sci Rep"},{"issue":"21","key":"12086_CR9","doi-asserted-by":"publisher","first-page":"7344","DOI":"10.1016\/j.eswa.2015.05.028","volume":"42","author":"L-L Chen","year":"2015","unstructured":"Chen L-L, Zhao Y, Zhang J, Zou J-Z (2015) Automatic detection of alertness\/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning. Expert Syst Appl 42(21):7344\u20137355. https:\/\/doi.org\/10.1016\/j.eswa.2015.05.028","journal-title":"Expert Syst Appl"},{"issue":"12","key":"12086_CR10","doi-asserted-by":"publisher","first-page":"4477","DOI":"10.3390\/s18124477","volume":"18","author":"M Ogino","year":"2018","unstructured":"Ogino M, Mitsukura Y (2018) Portable drowsiness detection through use of a prefrontal single-channel electroencephalogram. Sensors 18(12):4477. https:\/\/doi.org\/10.3390\/s18124477","journal-title":"Sensors"},{"key":"12086_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tim.2021.3094619","volume":"70","author":"VP Balam","year":"2021","unstructured":"Balam VP, Chinara S (2021) Statistical channel selection method for detecting drowsiness through single-channel EEG-based BCI system. IEEE Trans Instrum Meas 70:1\u20139. https:\/\/doi.org\/10.1109\/tim.2021.3094619","journal-title":"IEEE Trans Instrum Meas"},{"key":"12086_CR12","doi-asserted-by":"publisher","unstructured":"Siddhad G, Iwamura M, Roy PP (2025) Enhancing EEG Signal-Based Emotion Recognition with Synthetic Data: Diffusion Model Approach. IEEE Transactions on Artificial Intelligence, 1\u201310 https:\/\/doi.org\/10.1109\/TAI.2025.3641576http:\/\/arxiv.org\/abs\/2401.1687","DOI":"10.1109\/TAI.2025.3641576"},{"issue":"5","key":"12086_CR13","doi-asserted-by":"publisher","DOI":"10.3390\/s21051734","volume":"21","author":"S Chaabene","year":"2021","unstructured":"Chaabene S, Bouaziz B, Boudaya A, H\u00f6kelmann A, Ammar A, Chaari L (2021) Convolutional neural network for drowsiness detection using EEG signals. Sensors 21(5):1734. https:\/\/doi.org\/10.3390\/s21051734","journal-title":"Sensors"},{"issue":"17","key":"12086_CR14","doi-asserted-by":"publisher","first-page":"7624","DOI":"10.1109\/jsen.2019.2917850","volume":"19","author":"U Budak","year":"2019","unstructured":"Budak U, Bajaj V, Akbulut Y, Atila O, Sengur A (2019) An effective hybrid model for EEG-based drowsiness detection. IEEE Sens J 19(17):7624\u20137631. https:\/\/doi.org\/10.1109\/jsen.2019.2917850","journal-title":"IEEE Sens J"},{"key":"12086_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102364","volume":"65","author":"M Turkoglu","year":"2021","unstructured":"Turkoglu M, Alcin OF, Aslan M, Al-Zebari A, Sengur A (2021) Deep rhythm and long short term memory-based drowsiness detection. Biomed Signal Process Control 65:102364. https:\/\/doi.org\/10.1016\/j.bspc.2020.102364","journal-title":"Biomed Signal Process Control"},{"key":"12086_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2023.1067095","volume":"17","author":"D Walther","year":"2023","unstructured":"Walther D, Viehweg J, Haueisen J, M\u00e4der P (2023) A systematic comparison of deep learning methods for EEG time series analysis. Front Neuroinform 17:1067095. https:\/\/doi.org\/10.3389\/fninf.2023.1067095","journal-title":"Front Neuroinform"},{"key":"12086_CR17","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1109\/tnsre.2024.3366930","volume":"32","author":"W Ding","year":"2024","unstructured":"Ding W, Liu A, Guan L, Chen X (2024) A novel data augmentation approach using mask encoding for deep learning-based asynchronous SSVEP-BCI. IEEE Trans Neural Syst Rehabil Eng 32:875\u2013886. https:\/\/doi.org\/10.1109\/tnsre.2024.3366930","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"12086_CR18","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1109\/tnsre.2021.3079505","volume":"29","author":"JR Paulo","year":"2021","unstructured":"Paulo JR, Pires G, Nunes UJ (2021) Cross-subject zero calibration driver\u2019s drowsiness detection: exploring spatiotemporal image encoding of EEG signals for convolutional neural network classification. IEEE Trans Neural Syst Rehabil Eng 29:905\u2013915. https:\/\/doi.org\/10.1109\/tnsre.2021.3079505","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"9","key":"12086_CR19","doi-asserted-by":"publisher","first-page":"17832","DOI":"10.3390\/s140917832","volume":"14","author":"S Samiee","year":"2014","unstructured":"Samiee S, Azadi S, Kazemi R, Nahvi A, Eichberger A (2014) Data fusion to develop a driver drowsiness detection system with robustness to signal loss. Sensors 14(9):17832\u201317847. https:\/\/doi.org\/10.3390\/s140917832","journal-title":"Sensors"},{"issue":"2","key":"12086_CR20","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1109\/tiv.2022.3224690","volume":"8","author":"E Perkins","year":"2022","unstructured":"Perkins E, Sitaula C, Burke M, Marzbanrad F (2022) Challenges of driver drowsiness prediction: the remaining steps to implementation. IEEE Trans Intell Veh 8(2):1319\u20131338. https:\/\/doi.org\/10.1109\/tiv.2022.3224690","journal-title":"IEEE Trans Intell Veh"},{"key":"12086_CR21","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.neuroimage.2018.03.032","volume":"174","author":"C-S Wei","year":"2018","unstructured":"Wei C-S, Lin Y-P, Wang Y-T, Lin C-T, Jung T-P (2018) A subject-transfer framework for obviating inter-and intra-subject variability in EEG-based drowsiness detection. Neuroimage 174:407\u2013419. https:\/\/doi.org\/10.1016\/j.neuroimage.2018.03.032","journal-title":"Neuroimage"},{"key":"12086_CR22","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2020.553352","volume":"14","author":"J LaRocco","year":"2020","unstructured":"LaRocco J, Le MD, Paeng D-G (2020) A systemic review of available low-cost EEG headsets used for drowsiness detection. Front Neuroinform 14:553352. https:\/\/doi.org\/10.3389\/fninf.2020.553352","journal-title":"Front Neuroinform"},{"key":"12086_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2020.108927","volume":"347","author":"S Chinara","year":"2021","unstructured":"Chinara S et al (2021) Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal. J Neurosci Methods 347:108927. https:\/\/doi.org\/10.1016\/j.jneumeth.2020.108927","journal-title":"J Neurosci Methods"},{"key":"12086_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/taffc.2022.3169001","author":"Y Ding","year":"2022","unstructured":"Ding Y, Robinson N, Zhang S, Zeng Q, Guan C (2022) TSception: capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition. IEEE Trans Affect Comput. https:\/\/doi.org\/10.1109\/taffc.2022.3169001","journal-title":"IEEE Trans Affect Comput"},{"issue":"2","key":"12086_CR25","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aa5a98","volume":"14","author":"W-L Zheng","year":"2017","unstructured":"Zheng W-L, Lu B-L (2017) A multimodal approach to estimating vigilance using EEG and forehead EOG. J Neural Eng 14(2):026017. https:\/\/doi.org\/10.1088\/1741-2552\/aa5a98","journal-title":"J Neural Eng"},{"issue":"11","key":"12086_CR26","doi-asserted-by":"publisher","first-page":"2106","DOI":"10.1109\/tnsre.2018.2872924","volume":"26","author":"WL Lim","year":"2018","unstructured":"Lim WL, Sourina O, Wang LP (2018) STEW: Simultaneous Task EEG Workload Data Set. IEEE Trans Neural Syst Rehabil Eng 26(11):2106\u20132114. https:\/\/doi.org\/10.1109\/tnsre.2018.2872924","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"12086_CR27","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-Learn: Machine Learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"12086_CR28","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/bf00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-Vector Networks. Mach Learn 20:273\u2013297. https:\/\/doi.org\/10.1007\/bf00994018","journal-title":"Mach Learn"},{"issue":"5","key":"12086_CR29","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"VJ Lawhern","year":"2018","unstructured":"Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ (2018) EEGNet: A Compact Convolutional Neural Network for EEG-Based Brain-Computer Interfaces. J Neural Eng 15(5):056013. https:\/\/doi.org\/10.1088\/1741-2552\/aace8c","journal-title":"J Neural Eng"},{"key":"12086_CR30","doi-asserted-by":"publisher","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A ConvNet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986. https:\/\/doi.org\/10.1109\/cvpr52688.2022.01167","DOI":"10.1109\/cvpr52688.2022.01167"},{"key":"12086_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.120209","volume":"276","author":"Z Miao","year":"2023","unstructured":"Miao Z, Zhao M, Zhang X, Ming D (2023) LMDA-Net: A Lightweight Multi-Dimensional Attention Network for General EEG-Based Brain-Computer Interfaces and Interpretability. Neuroimage 276:120209. https:\/\/doi.org\/10.1016\/j.neuroimage.2023.120209","journal-title":"Neuroimage"},{"key":"12086_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105488","volume":"87","author":"G Siddhad","year":"2024","unstructured":"Siddhad G, Gupta A, Dogra DP, Roy PP (2024) Efficacy of Transformer Networks for Classification of EEG Data. Biomed Signal Process Control 87:105488. https:\/\/doi.org\/10.1016\/j.bspc.2023.105488http:\/\/arxiv.org\/abs\/2202.05170","journal-title":"Biomed Signal Process Control"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-12086-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-026-12086-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-12086-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T04:45:29Z","timestamp":1777956329000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-026-12086-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":32,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["12086"],"URL":"https:\/\/doi.org\/10.1007\/s00521-026-12086-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"1 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"360"}}