{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T17:57:16Z","timestamp":1768240636787,"version":"3.49.0"},"reference-count":27,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"name":"Inner Mongolia Autonomous Region Natural Science Foundation","award":["2025QN06022"],"award-info":[{"award-number":["2025QN06022"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62506177"],"award-info":[{"award-number":["62506177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"fund of Supporting the Reform and Development of Local Universities (Disciplinary Construction) and the special research project of First-class Discipline of Inner Mongolia A. R. of China","award":["YLXKZX-ND-036"],"award-info":[{"award-number":["YLXKZX-ND-036"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>Subjective measurement remains one of the most widely used approaches for evaluating Quality of Experience (QoE) in tactile virtual environments. However, its reliability is often compromised by factors such as conscious bias, variations in user expressiveness, and contextual influences, which may distort the accuracy of evaluation outcomes. In light of the fact that Electroencephalography (EEG) provides a direct window into neurophysiological correlates of emotion and cognitive states, this article proposes an objective QoE evaluation method for Virtual Reality (VR) with vibrotactile feedback based on Inter-user Brain Spatial Information (IBSI). The proposed IBSI feature extraction method enhances the conventional Common Spatial Pattern (CSP) algorithm through the introduction of a regularization constraint term designed to mitigate overfitting. Moreover, the covariance matrix of each non-target user is weighted according to its Kullback\u2013Leibler divergence from the target user, enhancing cross-user alignment and supporting effective transfer of neural information. To validate the performance of the proposed method, we design a VR shooting interaction experiment involving 64 participants. The study comprises three main phases: preparation, interaction, and subjective feedback. During the preparation phase, participants receive task explanations and familiarize themselves with the VR environment. In the interaction phase, participants complete a standardized shooting task, while EEG data are recorded synchronously. Finally, subjective feedback is collected through questionnaires. QoE assessment is accomplished by classifying IBSI features using classical classifiers, with subjective QoE ratings serving as ground truth. Experimental results show that our method outperforms existing methods in classification accuracy, and the dominant activation pattern in the alpha rhythm is consistent with neural mechanisms associated with motor perception. Meanwhile, the mutual interpretability between subjective and objective data characterizing QoE further validates the rationality of the experimental paradigm.<\/jats:p>","DOI":"10.1145\/3777459","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T16:05:03Z","timestamp":1763568303000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["QoE Evaluation for VR with Vibrotactile Feedback Based on Inter-user Brain Spatial Information"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5522-5851","authenticated-orcid":false,"given":"Yan","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science, Inner Mongolia University, Hohhot, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0399-4659","authenticated-orcid":false,"given":"Rui","family":"Song","sequence":"additional","affiliation":[{"name":"School of Future Technology and School of Artificial Intelligence, Dalian University of Technology, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2184-4360","authenticated-orcid":false,"given":"Riting","family":"Xia","sequence":"additional","affiliation":[{"name":"College of Computer Science, Inner Mongolia University, Hohhot, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4772-3172","authenticated-orcid":false,"given":"Zhenwei","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Computer Science, Inner Mongolia University, Hohhot, China and State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3588037.3595400"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/2540991"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhcs.2024.103312"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2020.3025792"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TOH.2023.3303838"},{"key":"e_1_3_1_7_2","volume-title":"Definition of Quality of Experience (QoE)","author":"International Telecommunication Union","year":"2007","unstructured":"International Telecommunication Union. 2007. Definition of Quality of Experience (QoE). ITU-T Recommendation P.10\/G.100 (Amendment 1). ITU, Geneva, Switzerland."},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3303080"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3463825"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3650208"},{"key":"e_1_3_1_11_2","volume-title":"Mean Opinion Score (MOS) Terminology","author":"International Telecommunication Union","year":"2016","unstructured":"International Telecommunication Union. 2016. Mean Opinion Score (MOS) Terminology. ITU-R Recommendation P.800.1. 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Interpretable Machine Learning. Chapter 5: Feature Importance. Retrieved from https:\/\/christophm.github.io\/interpretable-ml-book\/"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3777459","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T14:31:24Z","timestamp":1768228284000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3777459"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,12]]},"references-count":27,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,31]]}},"alternative-id":["10.1145\/3777459"],"URL":"https:\/\/doi.org\/10.1145\/3777459","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,12]]},"assertion":[{"value":"2024-12-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}