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Tracking Feature-based Attention

Overview
Journal J Neural Eng
Date 2018 Dec 8
PMID 30524109
Citations 3
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Abstract

Objective: 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).

Approach: 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.

Main Results: 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.

Significance: 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.

Citing Articles

Optimising the classification of feature-based attention in frequency-tagged electroencephalography data.

Renton A, Painter D, Mattingley J Sci Data. 2022; 9(1):296.

PMID: 35697741 PMC: 9192640. DOI: 10.1038/s41597-022-01398-z.


Attention Detection in Virtual Environments Using EEG Signals: A Scoping Review.

Souza R, Naves E Front Physiol. 2021; 12:727840.

PMID: 34887770 PMC: 8650681. DOI: 10.3389/fphys.2021.727840.


Steady-State Visually Evoked Potentials and Feature-based Attention: Preregistered Null Results and a Focused Review of Methodological Considerations.

Adam K, Chang L, Rangan N, Serences J J Cogn Neurosci. 2021; 33(4):695-724.

PMID: 33416444 PMC: 8354379. DOI: 10.1162/jocn_a_01665.