{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T01:00:36Z","timestamp":1771462836113,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T00:00:00Z","timestamp":1727568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Basic Research Plan","award":["JCKY2022602C024"],"award-info":[{"award-number":["JCKY2022602C024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Brain\u2013computer interface (BCI) offers a novel means of communication and control for individuals with disabilities and can also enhance the interactions between humans and machines for the broader population. This paper explores the brain neural signatures of unmanned aerial vehicle (UAV) operators in emergencies and develops an operator\u2019s electroencephalography (EEG) signals-based detection method for UAV emergencies. We found regularity characteristics similar to classic event-related potential (ERP) components like visual mismatch negativity (vMMN) and contingent negative variation (CNV). Source analysis revealed a sequential activation of the occipital, temporal, and frontal lobes following the onset of emergencies, corresponding to the processing of attention, emotion, and motor intention triggered by visual stimuli. Furthermore, an online detection system was implemented and tested. Experimental results showed that the system achieved an average accuracy of over 88% in detecting emergencies with a detection latency of 431.95 ms from the emergency onset. This work lays a foundation for understanding the brain activities of operators in emergencies and developing an EEG-based detection method for emergencies to assist UAV operations.<\/jats:p>","DOI":"10.3390\/s24196304","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T07:19:37Z","timestamp":1727680777000},"page":"6304","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Neural Signature and Decoding of Unmanned Aerial Vehicle Operators in Emergency Scenarios Using Electroencephalography"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5922-8655","authenticated-orcid":false,"given":"Manyu","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Aberham Genetu","family":"Feleke","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3188-1384","authenticated-orcid":false,"given":"Weijie","family":"Fei","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Luzheng","family":"Bi","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/S1388-2457(02)00057-3","article-title":"Brain\u2013computer interfaces for communication and control","volume":"113","author":"Wolpaw","year":"2002","journal-title":"Clin. Neurophysiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1109\/TSMC.2022.3212744","article-title":"Multitask-oriented brain-controlled intelligent vehicle based on human\u2013machine intelligence integration","volume":"53","author":"Wang","year":"2022","journal-title":"IEEE Trans. Syst. Man. Cybern. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1113\/jphysiol.2006.125633","article-title":"Brain\u2013computer interfaces: Communication and restoration of movement in paralysis","volume":"579","author":"Birbaumer","year":"2007","journal-title":"J. Physiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1109\/TNSRE.2024.3379451","article-title":"Robust decoding of upper-limb movement direction under cognitive distraction with invariant patterns in embedding manifold","volume":"32","author":"Peng","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.1109\/TBME.2020.3034112","article-title":"Decoding single-hand and both-hand movement directions from noninvasive neural signals","volume":"68","author":"Wang","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1109\/TNSRE.2024.3406371","article-title":"Neural Correlate and Movement Decoding of Simultaneous-and-Sequential Bimanual Movements Using EEG Signals","volume":"32","author":"Wang","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Das, R., Iversen, H.K., and Puthusserypady, S. (2020). Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application. Comput. Biol. Med., 123.","DOI":"10.1016\/j.compbiomed.2020.103843"},{"key":"ref_8","first-page":"161","article-title":"Combining brain\u2013computer interfaces and assistive technologies: State-of-the-art and challenges","volume":"4","author":"Rupp","year":"2010","journal-title":"Front. Neurosci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"036010","DOI":"10.1088\/1741-2552\/ab882e","article-title":"Decoding hand movements from human EEG to control a robotic arm in a simulation environment","volume":"17","author":"Schwarz","year":"2020","journal-title":"J. Neural Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"122954","DOI":"10.1016\/j.eswa.2023.122954","article-title":"EEG sensor driven assistive device for elbow and finger rehabilitation using deep learning","volume":"244","author":"Mukherjee","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1080\/10447318.2019.1612213","article-title":"Brain\u2013computer interface games based on consumer-grade EEG Devices: A systematic literature review","volume":"36","author":"Vasiljevic","year":"2020","journal-title":"Int. J. Hum.-Comput. Interact."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Tejada, L.A., Puertas-Gonz\u00e1lez, A., Yoshimura, N., and Koike, Y. (2021). Exploring EEG Characteristics to Identify Emotional Reactions under Videogame Scenarios. Brain Sci., 11.","DOI":"10.3390\/brainsci11030378"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ko, L.W., Chang, Y., Wu, P.L., Tzou, H.A., Chen, S.F., Tang, S.C., Yeh, C.L., and Chen, Y.J. (2019). Development of a smart helmet for strategical BCI applications. Sensors, 19.","DOI":"10.3390\/s19081867"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zandbagleh, A., Sanei, S., and Azami, H. (2024). Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives. Sensors, 24.","DOI":"10.3390\/s24186103"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.neubiorev.2019.07.021","article-title":"Depression biomarkers using non-invasive EEG: A review","volume":"105","author":"Rosa","year":"2019","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.chb.2017.12.037","article-title":"Review on portable EEG technology in educational research","volume":"81","author":"Xu","year":"2018","journal-title":"Comput. Hum. Behav."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.compedu.2018.03.020","article-title":"Mental effort detection using EEG data in E-learning contexts","volume":"122","author":"Lin","year":"2018","journal-title":"Comput. Educ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/S0304-3940(98)00122-0","article-title":"Induced alpha band power changes in the human EEG and attention","volume":"244","author":"Klimesch","year":"1998","journal-title":"Neurosci. Lett."},{"key":"ref_19","first-page":"1","article-title":"EEG based emotion recognition: A tutorial and review","volume":"55","author":"Li","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1016\/j.eswa.2007.12.043","article-title":"Using EEG spectral components to assess algorithms for detecting fatigue","volume":"36","author":"Jap","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"056001","DOI":"10.1088\/1741-2560\/8\/5\/056001","article-title":"EEG potentials predict upcoming emergency brakings during simulated driving","volume":"8","author":"Haufe","year":"2011","journal-title":"J. Neural Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"016001","DOI":"10.1088\/1741-2560\/12\/1\/016001","article-title":"Detection of braking intention in diverse situations during simulated driving based on EEG feature combination","volume":"12","author":"Kim","year":"2014","journal-title":"J. Neural Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1926","DOI":"10.1109\/TNSRE.2018.2868486","article-title":"A novel method of emergency situation detection for a brain-controlled vehicle by combining EEG signals with surrounding information","volume":"26","author":"Bi","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"51861","DOI":"10.1109\/ACCESS.2023.3262668","article-title":"A survey of indoor uav obstacle avoidance research","volume":"11","author":"Li","year":"2023","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1109\/TNSRE.2012.2233757","article-title":"A brain\u2013machine interface to navigate a mobile robot in a planar workspace: Enabling humans to fly simulated aircraft with EEG","volume":"21","author":"Akce","year":"2012","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tothong, T., Samawi, J., Govalkar, A., and George, K. (2021, January 9\u201311). Brain-Computer Interface for Quadcopter Morphology Manipulation. Proceedings of the 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India.","DOI":"10.1109\/CONECCT52877.2021.9622548"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chung, M.A., Lin, C.W., and Chang, C.T. (2021). The human\u2014Unmanned aerial vehicle system based on SSVEP\u2014Brain computer interface. Electronics, 10.","DOI":"10.3390\/electronics10233025"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Belkacem, A.N., and Lakas, A. (July, January 28). A cooperative EEG-based BCI control system for robot\u2013drone interaction. Proceedings of the 2021 International Wireless Communications and Mobile Computing (IWCMC), Harbin, China.","DOI":"10.1109\/IWCMC51323.2021.9498781"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shi, J., Xu, X., Bi, L., Feleke, A.G., and Fei, W. (2022). Low-quality Video Target Detection Based on EEG Signal using Eye Movement Alignment. Cyborg Bionic Syst., 5.","DOI":"10.34133\/cbsystems.0121"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.tics.2012.10.007","article-title":"Alpha-band oscillations, attention, and controlled access to stored information","volume":"16","author":"Klimesch","year":"2012","journal-title":"Trends Cogn. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zeng, X., Ji, L., Liu, Y., Zhang, Y., and Fu, S. (2022). Visual mismatch negativity reflects enhanced response to the deviant: Evidence from event-related potentials and electroencephalogram time-frequency analysis. Front. Hum. Neurosci., 16.","DOI":"10.3389\/fnhum.2022.800855"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Stefanics, G., Kreml\u00e1\u010dek, J., and Czigler, I. (2014). Visual mismatch negativity: A predictive coding view. Front. Hum. Neurosci., 8.","DOI":"10.3389\/fnhum.2014.00666"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1037\/h0032177","article-title":"Contingent negative variation (CNV) and psychological processes in man","volume":"77","author":"Tecce","year":"1972","journal-title":"Psychol. Bull."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.neuroimage.2014.03.063","article-title":"Trial-by-trial fluctuations in CNV amplitude reflect anticipatory adjustment of response caution","volume":"96","author":"Boehm","year":"2014","journal-title":"NeuroImage"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/TNSRE.2014.2304884","article-title":"Sliding HDCA: Single-trial EEG classification to overcome and quantify temporal variability","volume":"22","author":"Marathe","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kovarski, K., Latinus, M., Charpentier, J., Cl\u00e9ry, H., Roux, S., Houy-Durand, E., Saby, A., Bonnet-Brilhault, F., Batty, M., and Gomot, M. (2017). Facial expression related vMMN: Disentangling emotional from neutral change detection. Front. Hum. Neurosci., 11.","DOI":"10.3389\/fnhum.2017.00018"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.ijpsycho.2019.08.010","article-title":"Enhanced processing of facial emotion for target stimuli","volume":"146","author":"Rosburg","year":"2019","journal-title":"Int. J. Psychophysiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103150","DOI":"10.1016\/j.concog.2021.103150","article-title":"Subjectively different emotional schematic faces not automatically discriminated from the brain\u2019s bioelectrical responses","volume":"93","author":"Kask","year":"2021","journal-title":"Conscious. Cogn."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6304\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:06:38Z","timestamp":1760112398000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6304"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,29]]},"references-count":38,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24196304"],"URL":"https:\/\/doi.org\/10.3390\/s24196304","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,29]]}}}