NeuroGaze: A Hybrid EEG and Eye-Tracking Brain-Computer Interface for Hands-Free Interaction in Virtual Reality
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912579034546176 |
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| author | Coutray, Kyle Barbel, Wanyea Groth, Zack LaViola Jr, Joseph J |
| author_facet | Coutray, Kyle Barbel, Wanyea Groth, Zack LaViola Jr, Joseph J |
| contents | Brain-Computer Interfaces (BCIs) have traditionally been studied in clinical and laboratory contexts, but the rise of consumer-grade devices now allows exploration of their use in daily activities. Virtual reality (VR) provides a particularly relevant domain, where existing input methods often force trade-offs between speed, accuracy, and physical effort. This study introduces NeuroGaze, a hybrid interface combining electroencephalography (EEG) with eye tracking to enable hands-free interaction in immersive VR. Twenty participants completed a 360° cube-selection task using three different input methods: VR controllers, gaze combined with a pinch gesture, and NeuroGaze. Performance was measured by task completion time and error rate, while workload was evaluated using the NASA Task Load Index (NASA-TLX). NeuroGaze successfully supported target selection with off-the-shelf hardware, producing fewer errors than the alternative methods but requiring longer completion times, reflecting a classic speed-accuracy tradeoff. Workload analysis indicated reduced physical demand for NeuroGaze compared to controllers, though overall ratings and user preferences were mixed. These findings demonstrate the feasibility of hybrid EEG+gaze systems for everyday VR use, highlighting their ergonomic benefits and inclusivity potential. Although not yet competitive in speed, NeuroGaze points toward a practical role for consumer-grade BCIs in accessibility and long-duration applications, and underscores the need for improved EEG signal processing and adaptive multimodal integration to enhance future performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07863 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NeuroGaze: A Hybrid EEG and Eye-Tracking Brain-Computer Interface for Hands-Free Interaction in Virtual Reality Coutray, Kyle Barbel, Wanyea Groth, Zack LaViola Jr, Joseph J Human-Computer Interaction Brain-Computer Interfaces (BCIs) have traditionally been studied in clinical and laboratory contexts, but the rise of consumer-grade devices now allows exploration of their use in daily activities. Virtual reality (VR) provides a particularly relevant domain, where existing input methods often force trade-offs between speed, accuracy, and physical effort. This study introduces NeuroGaze, a hybrid interface combining electroencephalography (EEG) with eye tracking to enable hands-free interaction in immersive VR. Twenty participants completed a 360° cube-selection task using three different input methods: VR controllers, gaze combined with a pinch gesture, and NeuroGaze. Performance was measured by task completion time and error rate, while workload was evaluated using the NASA Task Load Index (NASA-TLX). NeuroGaze successfully supported target selection with off-the-shelf hardware, producing fewer errors than the alternative methods but requiring longer completion times, reflecting a classic speed-accuracy tradeoff. Workload analysis indicated reduced physical demand for NeuroGaze compared to controllers, though overall ratings and user preferences were mixed. These findings demonstrate the feasibility of hybrid EEG+gaze systems for everyday VR use, highlighting their ergonomic benefits and inclusivity potential. Although not yet competitive in speed, NeuroGaze points toward a practical role for consumer-grade BCIs in accessibility and long-duration applications, and underscores the need for improved EEG signal processing and adaptive multimodal integration to enhance future performance. |
| title | NeuroGaze: A Hybrid EEG and Eye-Tracking Brain-Computer Interface for Hands-Free Interaction in Virtual Reality |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.07863 |