{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T11:52:12Z","timestamp":1761306732985,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819520947"},{"type":"electronic","value":"9789819520954"}],"license":[{"start":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T00:00:00Z","timestamp":1761350400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T00:00:00Z","timestamp":1761350400000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-2095-4_33","type":"book-chapter","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T11:47:20Z","timestamp":1761306440000},"page":"397-407","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Task EEG Mental Workload Detection in\u00a0Aviation: An LSTM Framework Leveraging Task-Invariant Neural Signatures"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9777-9502","authenticated-orcid":false,"given":"Huanpeng","family":"Ye","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1694-4586","authenticated-orcid":false,"given":"Yumeng","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9304-4077","authenticated-orcid":false,"given":"Bo","family":"Lv","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4258-7463","authenticated-orcid":false,"given":"Peiru","family":"An","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3815-1432","authenticated-orcid":false,"given":"Yang","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,25]]},"reference":[{"issue":"4","key":"33_CR1","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1037\/0021-9010.73.4.621","volume":"73","author":"SL Kirmeyer","year":"1988","unstructured":"Kirmeyer, S.L.: Coping with competing demands: interruption and the type A pattern. J. Appl. Psychol. 73(4), 621 (1988)","journal-title":"J. Appl. Psychol."},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Shappell, S., Detwiler, C., Holcomb, K., et al.: Human error and commercial aviation accidents: an analysis using the human factors analysis and classification system. In: Human Error in Aviation, pp. 73\u201388. Routledge (2017)","DOI":"10.4324\/9781315092898-5"},{"key":"33_CR3","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.ergon.2018.12.001","volume":"69","author":"LE Reinerman-Jones","year":"2019","unstructured":"Reinerman-Jones, L.E., Hughes, N., D\u2019Agostino, A., et al.: Human performance metrics for the nuclear domain: a tool for evaluating measures of workload, situation awareness and teamwork. Int. J. Ind. Ergon. 69, 217\u2013227 (2019)","journal-title":"Int. J. Ind. Ergon."},{"key":"33_CR4","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.3389\/fphys.2017.01019","volume":"8","author":"T Rad\u00fcntz","year":"2017","unstructured":"Rad\u00fcntz, T.: Dual frequency head maps: a new method for indexing mental workload continuously during execution of cognitive tasks. Front. Physiol. 8, 1019 (2017)","journal-title":"Front. Physiol."},{"issue":"11","key":"33_CR5","doi-asserted-by":"publisher","first-page":"2259035","DOI":"10.1142\/S0218001422590352","volume":"36","author":"G Jiang","year":"2022","unstructured":"Jiang, G., Chen, H., Wang, C., et al.: Mental workload artificial intelligence assessment of pilots\u2019 EEG based on multi-dimensional data fusion and LSTM with attention mechanism model. Int. J. Pattern Recogn. Artif. Intell. 36(11), 2259035 (2022)","journal-title":"Int. J. Pattern Recogn. Artif. Intell."},{"key":"33_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ergon.2017.08.004","volume":"64","author":"X Wanyan","year":"2018","unstructured":"Wanyan, X., Zhuang, D., Lin, Y., et al.: Influence of mental workload on detecting information varieties revealed by mismatch negativity during flight simulation. Int. J. Ind. Ergon. 64, 1\u20137 (2018)","journal-title":"Int. J. Ind. Ergon."},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Zhang, K., Wu, K., et al.: Mental workload recognition using ECG and machine learning in simulated flight tasks. In: 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 1560\u20131565. IEEE (2022)","DOI":"10.1109\/IAEAC54830.2022.9930029"},{"key":"33_CR8","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.apergo.2015.07.009","volume":"52","author":"M Fallahi","year":"2016","unstructured":"Fallahi, M., Motamedzade, M., Heidarimoghadam, R., et al.: Effects of mental workload on physiological and subjective responses during traffic density monitoring: a field study. Appl. Ergon. 52, 95\u2013103 (2016)","journal-title":"Appl. Ergon."},{"issue":"1","key":"33_CR9","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1109\/JBHI.2022.3210019","volume":"27","author":"Y Xu","year":"2022","unstructured":"Xu, Y., Yu, Y., Xia, M., et al.: A novel and efficient surface electromyography decomposition algorithm using local spatial information. IEEE J. Biomed. Health Inf. 27(1), 286\u2013295 (2022)","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"33_CR10","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1109\/TNSRE.2024.3367742","volume":"32","author":"Y Xu","year":"2024","unstructured":"Xu, Y., Yu, Y., Zhao, Z., et al.: Decoding multi-DoF movements using a CST-based force generation model with single-DoF training. IEEE Trans. Neural Syst. Rehabil. Eng. 32, 974\u2013982 (2024)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"3","key":"33_CR11","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1109\/TCDS.2021.3090217","volume":"14","author":"Y Zhou","year":"2021","unstructured":"Zhou, Y., Huang, S., Xu, Z., et al.: Cognitive workload recognition using EEG signals and machine learning: a review. IEEE Trans. Cogn. Dev. Syst. 14(3), 799\u2013818 (2021)","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"33_CR12","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-3-030-79816-1_24","volume-title":"Advances in Human Factors and System Interactions","author":"S Belt","year":"2021","unstructured":"Belt, S., Gai, Y., Gururajan, S., Tamilselvan, G., Bollock, N.K.: Exploring pilot workload during professional pilot primary training and development: a feasibility study. In: Nunes, I.L. (ed.) AHFE 2021. LNNS, vol. 265, pp. 193\u2013201. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-79816-1_24"},{"key":"33_CR13","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1109\/TNSRE.2019.2913400","volume":"27","author":"P Zhang","year":"2019","unstructured":"Zhang, P., Wang, X., Chen, J., You, W., Zhang, W.: Spectral and temporal feature learning with two-stream neural networks for mental workload assessment. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 1149\u20131159 (2019)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"33_CR14","doi-asserted-by":"publisher","first-page":"121929","DOI":"10.1109\/ACCESS.2020.3006907","volume":"8","author":"DH Lee","year":"2020","unstructured":"Lee, D.H., Jeong, J.H., Kim, K., Yu, B.W., Lee, S.W.: Continuous EEG decoding of pilots\u2019 mental states using multiple feature block-based convolutional neural network. IEEE Access 8, 121929\u2013121941 (2020)","journal-title":"IEEE Access"},{"key":"33_CR15","doi-asserted-by":"publisher","first-page":"3907","DOI":"10.1109\/TIM.2018.2885608","volume":"68","author":"EQ Wu","year":"2019","unstructured":"Wu, E.Q., Peng, X., Zhang, C.Z., Lin, J., Sheng, R.S.: Pilots\u2019 fatigue status recognition using deep contractive autoencoder network. IEEE Trans. Instrum. Meas. 68, 3907\u20133919 (2019)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Graves, A., Graves, A.: Long short-term memory. Superv. Seq. Label. Recurrent Neural Netw. 37\u201345 (2012)","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Safari, M.R., Shalbaf, R., Bagherzadeh, S., et al.: Classification of mental workload with EEG analysis by using effective connectivity and a hybrid model of CNN and LSTM. Comput. Methods Biomech. Biomed. Eng. 1\u201315 (2024)","DOI":"10.1080\/10255842.2024.2386325"},{"key":"33_CR18","doi-asserted-by":"crossref","unstructured":"Sharma, S., Gupta, R., Kumar, J., et al.: EEG-based mental workload estimation using bidirectional LSTM. In: 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS), pp. 1082\u20131086. IEEE (2024)","DOI":"10.1109\/ICTACS62700.2024.10840874"},{"issue":"4","key":"33_CR19","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.3390\/s24041174","volume":"24","author":"A Hern\u00e1ndez-Sabat\u00e9","year":"2024","unstructured":"Hern\u00e1ndez-Sabat\u00e9, A., Yauri, J., Folch, P., et al.: EEG dataset collection for mental workload predictions in flight-deck environment. Sensors 24(4), 1174 (2024)","journal-title":"Sensors"},{"issue":"5","key":"33_CR20","doi-asserted-by":"publisher","first-page":"2298","DOI":"10.3390\/app12052298","volume":"12","author":"A Hern\u00e1ndez-Sabat\u00e9","year":"2022","unstructured":"Hern\u00e1ndez-Sabat\u00e9, A., Yauri, J., Folch, P., et al.: Recognition of the mental workloads of pilots in the cockpit using EEG signals. Appl. Sci. 12(5), 2298 (2022)","journal-title":"Appl. Sci."}],"container-title":["Lecture Notes in Computer Science","Intelligent Robotics and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-2095-4_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T11:47:24Z","timestamp":1761306444000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-2095-4_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,25]]},"ISBN":["9789819520947","9789819520954"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-2095-4_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,25]]},"assertion":[{"value":"25 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Robotics and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Okayama","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icira2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icira2025.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}