{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T19:36:35Z","timestamp":1783280195406,"version":"3.54.6"},"publisher-location":"Cham","reference-count":67,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032298386","type":"print"},{"value":"9783032298393","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-29839-3_10","type":"book-chapter","created":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T18:34:46Z","timestamp":1783276486000},"page":"149-172","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Toward Deployable Neural Directional Control: Design Patterns and\u00a0Evaluation Challenges"],"prefix":"10.1007","author":[{"given":"Cole","family":"Garrison","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masih","family":"Faryadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nawwaf","family":"Aleisa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,6]]},"reference":[{"key":"10_CR1","unstructured":"Aleisa, N., et al.: Accessible EEG game control: Real-time personalization with consumer-grade EEG hardware. In: International Conference on Human-Computer Interaction, pp. 120\u2013135 (2026)"},{"issue":"20","key":"10_CR2","doi-asserted-by":"publisher","first-page":"14681","DOI":"10.1007\/s00521-021-06352-5","volume":"35","author":"H Altaheri","year":"2023","unstructured":"Altaheri, H., et al.: Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review. Neural Comput. Appl. 35(20), 14681\u201314722 (2023)","journal-title":"Neural Comput. Appl."},{"issue":"21","key":"10_CR3","doi-asserted-by":"publisher","first-page":"12629","DOI":"10.1007\/s00521-024-09809-5","volume":"36","author":"Y Badr","year":"2024","unstructured":"Badr, Y., Tariq, U., Al-Shargie, F., Babiloni, F., Al Mughairbi, F., Al-Nashash, H.: A review on evaluating mental stress by deep learning using EEG signals. Neural Comput. Appl. 36(21), 12629\u201312654 (2024)","journal-title":"Neural Comput. Appl."},{"key":"10_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2022.109489","volume":"370","author":"Y Cai","year":"2022","unstructured":"Cai, Y., She, Q., Ji, J., Ma, Y., Zhang, J., Zhang, Y.: Motor imagery EEG decoding using manifold embedded transfer learning. J. Neurosci. Methods 370, 109489 (2022)","journal-title":"J. Neurosci. Methods"},{"issue":"2","key":"10_CR5","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1109\/TMRB.2025.3537663","volume":"7","author":"M Ceradini","year":"2025","unstructured":"Ceradini, M., Tortora, S., Micera, S., Tonin, L.: The effect of user learning for online EEG decoding of upper-limb movement intention. IEEE Trans. Med. Robot. Bionics 7(2), 633\u2013641 (2025)","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"key":"10_CR6","doi-asserted-by":"publisher","first-page":"1992","DOI":"10.1109\/TNSRE.2022.3191869","volume":"30","author":"Y Chen","year":"2022","unstructured":"Chen, Y., Yang, R., Huang, M., Wang, Z., Liu, X.: Single-source to single-target cross-subject motor imagery classification based on multisubdomain adaptation network. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 1992\u20132002 (2022)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"3","key":"10_CR7","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab0ab5","volume":"16","author":"A Craik","year":"2019","unstructured":"Craik, A., He, Y., Contreras-Vidal, J.L.: Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16(3), 031001 (2019)","journal-title":"J. Neural Eng."},{"issue":"1","key":"10_CR8","doi-asserted-by":"publisher","first-page":"5401","DOI":"10.1038\/s41467-025-61064-x","volume":"16","author":"Y Ding","year":"2025","unstructured":"Ding, Y., Udompanyawit, C., Zhang, Y., He, B.: Eeg-based brain-computer interface enables real-time robotic hand control at individual finger level. Nat. Commun. 16(1), 5401 (2025)","journal-title":"Nat. Commun."},{"key":"10_CR9","doi-asserted-by":"publisher","first-page":"0044","DOI":"10.34133\/cbsystems.0044","volume":"4","author":"Y Dong","year":"2023","unstructured":"Dong, Y., Wang, S., Huang, Q., Berg, R.W., Li, G., He, J.: Neural decoding for intracortical brain-computer interfaces. Cyborg Bionic Syst. 4, 0044 (2023)","journal-title":"Cyborg Bionic Syst."},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Dou, G., Zhou, Z., Qu, X.: Time majority voting, a pc-based EEG classifier for non-expert users. In: International Conference on Human-Computer Interaction, pp. 415\u2013428. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-17618-0_29"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Downey, J.E., et\u00a0al.: A roadmap for implanting microelectrode arrays to evoke tactile sensations through intracortical microstimulation. medRxiv (2024)","DOI":"10.1101\/2024.04.26.24306239"},{"key":"10_CR12","doi-asserted-by":"publisher","first-page":"42258","DOI":"10.52202\/075280-1831","volume":"36","author":"C Fan","year":"2023","unstructured":"Fan, C., et al.: Plug-and-play stability for intracortical brain-computer interfaces: a one-year demonstration of seamless brain-to-text communication. Adv. Neural. Inf. Process. Syst. 36, 42258\u201342270 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Forenzo, D., Zhang, Y., Wittenberg, G.F., He, B.: Continuous reaching and grasping with a BCI controlled robotic arm in healthy and stroke-affected individuals. IEEE Tran. Neural Syst. Rehabil. Eng. (2025)","DOI":"10.1101\/2025.04.16.25325551"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Gaspar-Figueiredo, D., Abrah\u00e3o, S., Insfr\u00e1n, E., Vanderdonckt, J.: Measuring user experience of adaptive user interfaces using EEG: a replication study. In: Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, pp. 52\u201361 (2023)","DOI":"10.1145\/3593434.3593452"},{"key":"10_CR15","doi-asserted-by":"publisher","first-page":"28119","DOI":"10.1109\/ACCESS.2023.3258969","volume":"11","author":"MT Ghodrati","year":"2023","unstructured":"Ghodrati, M.T., Mirfathollahi, A., Shalchyan, V., Daliri, M.R.: Intracortical hindlimb brain-computer interface systems: a systematic review. IEEE Access 11, 28119\u201328139 (2023)","journal-title":"IEEE Access"},{"key":"10_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fnrgo.2022.837307","volume":"3","author":"J Giles","year":"2022","unstructured":"Giles, J., Ang, K.K., Phua, K.S., Arvaneh, M.: A transfer learning algorithm to reduce brain-computer interface calibration time for long-term users. Front. Neuroergon. 3, 837307 (2022)","journal-title":"Front. Neuroergon."},{"key":"10_CR17","doi-asserted-by":"publisher","first-page":"95417","DOI":"10.1109\/ACCESS.2024.3424953","volume":"12","author":"K Glavas","year":"2024","unstructured":"Glavas, K., Tzimourta, K.D., Tzallas, A.T., Giannakeas, N., Tsipouras, M.G.: Empowering individuals with disabilities: a 4-DoF BCI wheelchair using mi and EOG signals. IEEE Access 12, 95417\u201395433 (2024)","journal-title":"IEEE Access"},{"key":"10_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109534","volume":"185","author":"I Hameed","year":"2025","unstructured":"Hameed, I., Khan, D.M., Ahmed, S.M., Aftab, S.S., Fazal, H.: Enhancing motor imagery EEG signal decoding through machine learning: a systematic review of recent progress. Comput. Biol. Med. 185, 109534 (2025)","journal-title":"Comput. Biol. Med."},{"key":"10_CR19","unstructured":"Hettick, M., et\u00a0al.: Minimally invasive implantation of scalable high-density cortical microelectrode arrays for multimodal neural decoding and stimulation. Nat. Biomed. Eng. 1\u201316 (2025)"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Hosman, T., Pun, T.K., Kapitonava, A., Simeral, J.D., Hochberg, L.R.: Months-long high-performance fixed LSTM decoder for cursor control in human intracortical brain-computer interfaces. In: 2023 11th International IEEE\/EMBS Conference on Neural Engineering (NER), pp.\u00a01\u20135. IEEE (2023)","DOI":"10.1109\/NER52421.2023.10123740"},{"key":"10_CR21","doi-asserted-by":"publisher","first-page":"1122661","DOI":"10.3389\/fnins.2023.1122661","volume":"17","author":"G Huang","year":"2023","unstructured":"Huang, G., et al.: Discrepancy between inter-and intra-subject variability in EEG-based motor imagery brain-computer interface: evidence from multiple perspectives. Front. Neurosci. 17, 1122661 (2023)","journal-title":"Front. Neurosci."},{"key":"10_CR22","doi-asserted-by":"publisher","first-page":"1431815","DOI":"10.3389\/fncom.2024.1431815","volume":"18","author":"W Jin","year":"2024","unstructured":"Jin, W., et al.: Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review. Front. Comput. Neurosci. 18, 1431815 (2024)","journal-title":"Front. Comput. Neurosci."},{"issue":"1","key":"10_CR23","doi-asserted-by":"publisher","first-page":"4662","DOI":"10.1038\/s41467-025-59652-y","volume":"16","author":"BM Karpowicz","year":"2025","unstructured":"Karpowicz, B.M., et al.: Stabilizing brain-computer interfaces through alignment of latent dynamics. Nat. Commun. 16(1), 4662 (2025)","journal-title":"Nat. Commun."},{"issue":"1","key":"10_CR24","doi-asserted-by":"publisher","first-page":"25775","DOI":"10.1038\/s41598-024-73755-4","volume":"14","author":"A Keutayeva","year":"2024","unstructured":"Keutayeva, A., Fakhrutdinov, N., Abibullaev, B.: Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs. Sci. Rep. 14(1), 25775 (2024)","journal-title":"Sci. Rep."},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Key, M.L., Mehtiyev, T., Qu, X.: Advancing EEG-based gaze prediction using depthwise separable convolution and enhanced pre-processing. In: International Conference on Human-Computer Interaction, pp. 3\u201317. Springer, Cham (2024)","DOI":"10.1007\/978-3-031-61572-6_1"},{"issue":"5","key":"10_CR26","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1007\/s42979-023-02160-x","volume":"4","author":"PK Khuntia","year":"2023","unstructured":"Khuntia, P.K., Manivannan, P.: Review of neural interfaces: means for establishing brain-machine communication. SN Comput. Sci. 4(5), 672 (2023)","journal-title":"SN Comput. Sci."},{"issue":"1","key":"10_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3712283","volume":"9","author":"MT Knierim","year":"2025","unstructured":"Knierim, M.T., Zimny, C., Ivucic, G., R\u00f6ddiger, T.: Advancing wearable BCI: headphone EEG for cognitive load detection in lab and field. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 9(1), 1\u201326 (2025)","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquit. Technol."},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Konkar, A., Qu, X.: A review of transformer-based and hybrid deep learning approaches for EEG analysis. In: International Conference on Human-Computer Interaction, pp. 391\u2013404. Springer, Cham (2026)","DOI":"10.1007\/978-3-032-12660-3_29"},{"key":"10_CR29","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1109\/TNSRE.2023.3259730","volume":"31","author":"A Li","year":"2023","unstructured":"Li, A., Wang, Z., Zhao, X., Xu, T., Zhou, T., Hu, H.: MDTL: a novel and model-agnostic transfer learning strategy for cross-subject motor imagery BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 1743\u20131753 (2023)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"1","key":"10_CR30","doi-asserted-by":"publisher","first-page":"10664","DOI":"10.1038\/s41598-025-95178-5","volume":"15","author":"J Li","year":"2025","unstructured":"Li, J., Shi, J., Yu, P., Yan, X., Lin, Y.: Feature-aware domain invariant representation learning for EEG motor imagery decoding. Sci. Rep. 15(1), 10664 (2025)","journal-title":"Sci. Rep."},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Li, W., Zhou, N., Qu, X.: Enhancing eye-tracking performance through multi-task learning transformer. In: International Conference on Human-Computer Interaction, pp. 31\u201346. Springer, Cham (2024)","DOI":"10.1007\/978-3-031-61572-6_3"},{"issue":"2","key":"10_CR32","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ad3986","volume":"21","author":"W Li","year":"2024","unstructured":"Li, W., et al.: Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition. J. Neural Eng. 21(2), 026038 (2024)","journal-title":"J. Neural Eng."},{"issue":"1","key":"10_CR33","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1038\/s41597-022-01647-1","volume":"9","author":"J Ma","year":"2022","unstructured":"Ma, J., Yang, B., Qiu, W., Li, Y., Gao, S., Xia, X.: A large EEG dataset for studying cross-session variability in motor imagery brain-computer interface. Sci. Data 9(1), 531 (2022)","journal-title":"Sci. Data"},{"issue":"1","key":"10_CR34","doi-asserted-by":"publisher","first-page":"4714","DOI":"10.1038\/s41598-024-55413-x","volume":"14","author":"V Mondini","year":"2024","unstructured":"Mondini, V., Sburlea, A.I., M\u00fcller-Putz, G.R.: Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing. Sci. Rep. 14(1), 4714 (2024)","journal-title":"Sci. Rep."},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Murungi, N.K., Pham, M.V., Dai, X., Qu, X.: Trends in machine learning and electroencephalogram (EEG): a review for undergraduate researchers. In: International Conference on Human-Computer Interaction, pp. 426\u2013443. Springer, Cham (2023)","DOI":"10.1007\/978-3-031-48038-6_27"},{"key":"10_CR36","unstructured":"Murungi, N.K., Pham, M.V., Dai, X.C., Qu, X.: Empowering computer science students in electroencephalography (EEG) analysis: a review of machine learning algorithms for EEG datasets. In: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2023)"},{"issue":"1","key":"10_CR37","doi-asserted-by":"publisher","first-page":"6699","DOI":"10.1038\/s41598-023-33441-3","volume":"13","author":"J Nakuci","year":"2023","unstructured":"Nakuci, J., et al.: Within-subject reproducibility varies in multi-modal, longitudinal brain networks. Sci. Rep. 13(1), 6699 (2023)","journal-title":"Sci. Rep."},{"issue":"6","key":"10_CR38","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.3390\/s19061423","volume":"19","author":"N Padfield","year":"2019","unstructured":"Padfield, N., Zabalza, J., Zhao, H., Masero, V., Ren, J.: EEG-based brain-computer interfaces using motor-imagery: techniques and challenges. Sensors 19(6), 1423 (2019)","journal-title":"Sensors"},{"key":"10_CR39","unstructured":"Page, M.J., et\u00a0al.: The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372 (2021)"},{"issue":"18","key":"10_CR40","doi-asserted-by":"publisher","first-page":"6285","DOI":"10.3390\/s21186285","volume":"21","author":"A Palumbo","year":"2021","unstructured":"Palumbo, A., Gramigna, V., Calabrese, B., Ielpo, N.: Motor-imagery EEG-based BCIs in wheelchair movement and control: a systematic literature review. Sensors 21(18), 6285 (2021)","journal-title":"Sensors"},{"key":"10_CR41","doi-asserted-by":"publisher","first-page":"1429130","DOI":"10.3389\/fnhum.2024.1429130","volume":"18","author":"H Pan","year":"2024","unstructured":"Pan, H., et al.: Comprehensive evaluation methods for translating BCI into practical applications: usability, user satisfaction and usage of online BCI systems. Front. Hum. Neurosci. 18, 1429130 (2024)","journal-title":"Front. Hum. Neurosci."},{"key":"10_CR42","doi-asserted-by":"crossref","unstructured":"Patel, R., Zhu, Z., Bryson, B., Carlson, T., Jiang, D., Demosthenous, A.: Advancing EEG classification for neurodegenerative conditions using BCI: a graph attention approach with phase synchrony. Neuroelectronics 2(1) (2025)","DOI":"10.55092\/neuroelectronics20250001"},{"issue":"1","key":"10_CR43","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.1038\/s42003-024-06784-4","volume":"7","author":"TK Pun","year":"2024","unstructured":"Pun, T.K., et al.: Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces. Commun. Biol. 7(1), 1363 (2024)","journal-title":"Commun. Biol."},{"key":"10_CR44","unstructured":"Qu, X., et\u00a0al.: A comprehensive review of AI agents: transforming possibilities in technology and beyond. arXiv preprint arXiv:2508.11957 (2025)"},{"key":"10_CR45","doi-asserted-by":"crossref","unstructured":"Qu, X., Hickey, T.J.: Eeg4home: a human-in-the-loop machine learning model for EEG-based BCI. In: International Conference on Human-Computer Interaction, pp. 162\u2013172. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-05457-0_14"},{"key":"10_CR46","doi-asserted-by":"crossref","unstructured":"Qu, X., Key, M., Luo, E., Qiu, C.: Integrating HCI datasets in project-based machine learning courses: a college-level review and case study. In: International Conference on Human-Computer Interaction, pp. 124\u2013143. Springer, Cham (2024)","DOI":"10.1007\/978-3-031-76827-9_8"},{"key":"10_CR47","doi-asserted-by":"crossref","unstructured":"Qu, X., Liu, P., Li, Z., Hickey, T.: Multi-class time continuity voting for EEG classification. In: International Conference on Brain Function Assessment in Learning, pp. 24\u201333. Springer, Cham (2020)","DOI":"10.1007\/978-3-030-60735-7_3"},{"key":"10_CR48","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2020.541659","volume":"11","author":"X Qu","year":"2020","unstructured":"Qu, X., Liukasemsarn, S., Tu, J., Higgins, A., Hickey, T.J., Hall, M.H.: Identifying clinically and functionally distinct groups among healthy controls and first episode psychosis patients by clustering on EEG patterns. Front. Psych. 11, 541659 (2020)","journal-title":"Front. Psych."},{"key":"10_CR49","doi-asserted-by":"crossref","unstructured":"Qu, X., Sherwood, J., Liu, P., Aleisa, N.: Generative ai tools in higher education: a meta-analysis of cognitive impact. In: Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, pp.\u00a01\u20139 (2025)","DOI":"10.1145\/3706599.3719841"},{"issue":"5","key":"10_CR50","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, T.H., Faubert, J.: Deep learning-based electroencephalography analysis: a systematic review. J. Neural Eng. 16(5), 051001 (2019)","journal-title":"J. Neural Eng."},{"issue":"5","key":"10_CR51","doi-asserted-by":"publisher","first-page":"2798","DOI":"10.3390\/s23052798","volume":"23","author":"A Saibene","year":"2023","unstructured":"Saibene, A., Caglioni, M., Corchs, S., Gasparini, F.: EEG-based BCIs on motor imagery paradigm using wearable technologies: a systematic review. Sensors 23(5), 2798 (2023)","journal-title":"Sensors"},{"key":"10_CR52","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1109\/TNSRE.2024.3360194","volume":"32","author":"S Sartipi","year":"2024","unstructured":"Sartipi, S., Cetin, M.: Subject-independent deep architecture for EEG-based motor imagery classification. IEEE Trans. Neural Syst. Rehabil. Eng. 32, 718\u2013727 (2024)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10_CR53","unstructured":"Saunders, T., Aleisa, N., Wield, J., Sherwood, J., Qu, X.: Optimizing the literature review process: evaluating generative ai models on summarizing undergraduate data science research papers. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2024)"},{"issue":"1","key":"10_CR54","doi-asserted-by":"publisher","first-page":"41040","DOI":"10.1038\/s41598-025-24972-y","volume":"15","author":"O Sen","year":"2025","unstructured":"Sen, O., et al.: A low-latency neural inference framework for real-time handwriting recognition from EEG signals on an edge device. Sci. Rep. 15(1), 41040 (2025)","journal-title":"Sci. Rep."},{"key":"10_CR55","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TNSRE.2023.3241846","volume":"31","author":"Q She","year":"2023","unstructured":"She, Q., Chen, T., Fang, F., Zhang, J., Gao, Y., Zhang, Y.: Improved domain adaptation network based on wasserstein distance for motor imagery EEG classification. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 1137\u20131148 (2023)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10_CR56","doi-asserted-by":"crossref","unstructured":"Wang, R., Qu, X.: EEG daydreaming, a machine learning approach to detect daydreaming activities. In: International Conference on Human-Computer Interaction, pp. 202\u2013212. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-05457-0_17"},{"key":"10_CR57","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xie, W., Dou, S., Qu, X.: EMIGC: An EEG motor imagery controller for real-time gameplay. In: International Conference on Human-Computer Interaction, pp. 63\u201376. Springer, Cham (2025)","DOI":"10.1007\/978-3-032-12764-8_7"},{"key":"10_CR58","doi-asserted-by":"crossref","unstructured":"Welihinda, D., Gunarathne, L., Herath, H., Yasakethu, S., Madusanka, N., Lee, B.I.: EEG and EMG-based human-machine interface for navigation of mobility-related assistive wheelchair (MRA-w). Heliyon 10(6) (2024)","DOI":"10.1016\/j.heliyon.2024.e27777"},{"key":"10_CR59","unstructured":"Xie, W., Dou, J., Wang, Y., Qu, X.: From theory to play: a review of EEG-controlled directional games and evaluation of custom-developed BCI games. In: International Conference on Human-Computer Interaction, pp. 179\u2013191 (2025)"},{"key":"10_CR60","doi-asserted-by":"crossref","unstructured":"Xie, W., Wang, Y., Dou, S., Qu, X.: A systematic review of consumer-grade EEG applications in directional game control via motor imagery. In: International Conference on Human-Computer Interaction, pp. 85\u2013105. Springer, Cham (2026)","DOI":"10.1007\/978-3-032-12764-8_9"},{"key":"10_CR61","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.csbj.2023.07.033","volume":"22","author":"B Xu","year":"2023","unstructured":"Xu, B., Liu, D., Xue, M., Miao, M., Hu, C., Song, A.: Continuous shared control of a mobile robot with brain-computer interface and autonomous navigation for daily assistance. Comput. Struct. Biotechnol. J. 22, 3\u201316 (2023)","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10_CR62","doi-asserted-by":"crossref","unstructured":"Yi, L., Qu, X.: Attention-based CNN capturing EEG recording\u2019s average voltage and local change. In: International Conference on Human-Computer Interaction, pp. 448\u2013459. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-05643-7_29"},{"key":"10_CR63","doi-asserted-by":"publisher","first-page":"2824","DOI":"10.1109\/TNSRE.2022.3209155","volume":"30","author":"S Zhang","year":"2022","unstructured":"Zhang, S., et al.: Learning EEG representations with weighted convolutional siamese network: a large multi-session post-stroke rehabilitation study. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 2824\u20132833 (2022)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10_CR64","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1109\/TNSRE.2024.3358491","volume":"32","author":"Y Zhong","year":"2024","unstructured":"Zhong, Y., Yao, L., Pan, G., Wang, Y.: Cross-subject motor imagery decoding by transfer learning of tactile ERD. IEEE Trans. Neural Syst. Rehabil. Eng. 32, 662\u2013671 (2024)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10_CR65","doi-asserted-by":"publisher","first-page":"3163","DOI":"10.1109\/TNSRE.2023.3299350","volume":"31","author":"Y Zhou","year":"2023","unstructured":"Zhou, Y., et al.: Shared three-dimensional robotic arm control based on asynchronous BCI and computer vision. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 3163\u20133175 (2023)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10_CR66","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Dou, G., Qu, X.: Brainactivity1: a framework of EEG data collection and machine learning analysis for college students. In: International Conference on Human-Computer Interaction, pp. 119\u2013127. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-19679-9_16"},{"key":"10_CR67","doi-asserted-by":"crossref","unstructured":"Zhu, C., Xu, Y., Qu, X.: GVIT: combining convolutional and transformer layers for spatial-temporal EEG analysis. In: International Conference on Human-Computer Interaction, pp. 433\u2013442. Springer, Cham (2025)","DOI":"10.1007\/978-3-032-12660-3_32"}],"container-title":["Lecture Notes in Computer Science","Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-29839-3_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T18:35:00Z","timestamp":1783276500000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-29839-3_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032298386","9783032298393"],"references-count":67,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-29839-3_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"6 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montreal, QC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2026.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}