Towards Improving EEG-Based Intent Recognition in Visual Search Tasks

Sharma, Mansi; Rekrut, Maurice; Alexandersson, Jan; Krüger, Antonio

doi:10.1007/978-3-031-30111-7_51

Cited by 1 publication

(1 citation statement)

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“…Research efforts on passive BCIs has also sought to classify user intent. For instance, Sharma et al [102] achieved a 97.89% accuracy in discerning intent during free-viewing, target searching, and target presentation conditions, utilizing EEG features such as power spectral intensity and detrended fluctuation analysis. However, given the task's emphasis on visual searches, it's likely that the primary EEG components were the P300 wave or reward potentials [3,32].…”

Section: Passive Hybrid Bcismentioning

confidence: 99%

Towards an Eye-Brain-Computer Interface: Combining Gaze with the Stimulus-Preceding Negativity for Target Selections in XR

Reddy,

Proulx,

Hirshfield

et al. 2024

Preprint

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Gaze-assisted interaction techniques enable intuitive selections without requiring manual pointing but can result in unintended selections, known as Midas touch. A confirmation trigger eliminates this issue but requires additional physical and conscious user effort. Brain-computer interfaces (BCIs), particularly passive BCIs harnessing anticipatory potentials such as the Stimulus-Preceding Negativity (SPN) - evoked when users anticipate a forthcoming stimulus - present an effortless implicit solution for selection confirmation. Within a VR context, our research uniquely demonstrates that SPN has the potential to decode intent towards the visually focused target. We reinforce the scientific understanding of its mechanism by addressing a confounding factor - we demonstrate that the SPN is driven by the user’s intent to select the target, not by the stimulus feedback itself. Furthermore, we examine the effect of familiarly placed targets, finding that SPN may be evoked quicker as users acclimatize to target locations; a key insight for everyday BCIs.CCS CONCEPTSHuman-centered computing→Virtual reality;Mixed / augmented reality;Accessibility technologies;Interaction techniques.ACM Reference FormatG. S. Rajshekar Reddy, Michael J. Proulx, Leanne Hirshfield, and Anthony J. Ries. 2024. Towards an Eye-Brain-Computer Interface: Combining Gaze with the Stimulus-Preceding Negativity for Target Selections in XR. InProceedings of the CHI Conference on Human Factors in Computing Systems (CHI ‘24), May 11–16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 17 pages.https://doi.org/10.1145/3613904.3641925

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Section: Passive Hybrid Bcismentioning

confidence: 99%