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• W2899159277 abstract "Feature-based attention (FBA) helps one detect objects with a particular color, motion, or orientation. FBA works globally; the attended feature is enhanced at all positions in the visual field. This global property of FBA lets one use stimuli presented in the peripheral visual field to track attention in a task presented centrally. The present study explores the use of SSVEPs, generated by flicker presented peripherally, to track attention in a visual search task presented centrally. We evaluate whether this use of EEG to track FBA is robust enough to track attention when performing visual search within a dynamic 3D environment presented with a head-mounted display (HMD).Observers first performed a visual search task presented in the central visual field within a stationary virtual environment. The purpose of this first experiment was to establish whether flicker presented peripherally can produce SSVEPs during HMD use. The second experiment placed observers in a dynamic virtual environment in which observers moved around a racetrack. Peripheral flicker was again used to track attention to the color of the target in the visual search task.SSVEPs produced by flicker in the peripheral visual field are influenced strongly by attention in observers with stationary or moving viewpoints. Offline classification results show that one can track an observer's attended color, which suggests that these methods may provide a viable means for tracking FBA in a real-time task.Current FBA and brain-computer interface (BCI) studies primarily use foveal flicker to produce SSVEP responses. The present study's finding that one can use peripherally-presented flicker to track attention in dynamic virtual environments promises a more flexible and practical approach to BCIs based on FBA." @default.
• W2899159277 created "2018-11-09" @default.
• W2899159277 creator A5003706292 @default.
• W2899159277 creator A5072585175 @default.
• W2899159277 date "2019-01-09" @default.
• W2899159277 modified "2023-09-23" @default.
• W2899159277 title "Tracking feature-based attention" @default.
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• W2899159277 doi "https://doi.org/10.1088/1741-2552/aaed17" @default.
• W2899159277 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30524109" @default.
• W2899159277 hasPublicationYear "2019" @default.
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