Enhancing user experience in virtual reality through BCI-modulated pseudo-haptic feedback.
This study investigates the integration of brain-computer interface (BCI) technology with pseudo-haptic feedback to enhance adaptive interactions in virtual reality (VR). Using a NeuroSky headset, we developed a dual-layer control system combining blink detection (via SVM classifier) and attention-l...
| Publicado en: | Behaviour & Information Technology Vol. 45; no. 8; pp. 1661 - 1676 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
May2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193710035&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193710035 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0144929X B6Q jtl: Behaviour & Information Technology issn: 0144929X maglogo: Y pubinfo: dt: May2026 vid: 45 iid: 8 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 193710035 188085777 193710035 193710035 10.1080/0144929X.2025.2555323 193710035 ppf: 1661 ppct: 15 formats: tig: atl: Enhancing user experience in virtual reality through BCI-modulated pseudo-haptic feedback. aug: au: Teng, Jian Cho, Sukyoung Zhao, Sichong affil: School of Mechanical and Electrical Engineering, Lingnan Normal University, Zhanjiang, People's Republic of China sug: subj: Virtual Reality Brain-Computer Interfaces Feedback User-Computer Interface Touch Funding Source China Human Male Female Young Adult Experimental Studies ROC Curve Repeated Measures Analysis of Variance Pearson's Correlation Coefficient Data Analysis Software Descriptive Statistics Support Vector Machine Electroencephalography Attention Signal Processing, Computer Assisted Male Female ab: This study investigates the integration of brain-computer interface (BCI) technology with pseudo-haptic feedback to enhance adaptive interactions in virtual reality (VR). Using a NeuroSky headset, we developed a dual-layer control system combining blink detection (via SVM classifier) and attention-level modulation to operate 16 pseudo-haptic button configurations. Manifold learning (UMAP) and wavelet analysis revealed that sustained attention (>70%) dynamically adjusted haptic intensity (23% deeper protrusion) and beta-band (13–30 Hz) energy peaks predicted successful interactions 500 ms pre-trigger. Subjective evaluations showed proximity feedback with protrusion significantly improved embodiment (η² = 0.35), while hit effects boosted satisfaction (p < 0.01). BCI-driven adaptation achieved 82% user satisfaction – validating its necessity for personalised feedback. Results demonstrate BCI's capacity to align cognitive engagement with tactile realism, offering empirical guidelines for adaptive VR interfaces in neurorehabilitation and immersive training. Key metrics: 89.4% blink detection accuracy, 68.2% mean attention, task speed-attention correlation (r = 0.53). pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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