The Pursuing Gaze Beats Mouse in Non-Pop-Out Target Selection

Shishkin, Sergei L.; Velichkovsky, Boris M.; Melnichuk, Eugeny V.; Dubynin, Ignat A.; Zhao, Darisy G.; Isachenko, Andrey V.

doi:10.1109/smc.2018.00595

Cited by 4 publications

(11 citation statements)

References 17 publications

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“…The eye movement signals have also been capitalized on to realize hands-free intuitive interfaces for selecting the moving targets [7], [8], [9] or still targets [15]. In particular, our study concentrates on selecting still targets, and the development of such gazed-based interface suffers from Midas Touch problem [10], i.e., not every location we fixate is related to the target.…”

Section: Related Workmentioning

confidence: 99%

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Extended Control With Hybrid Gaze-BCI for Multi-Robot System Under Hands-Occupied Dual-Tasking

Zeng

Shen

Sun

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Currently there still remains a critical need of human involvements for multi-robot system (MRS) to successfully perform their missions in real-world applications, and the hand-controller has been commonly used for the operator to input MRS control commands. However, in more challenging scenarios involving concurrent MRS control and system monitoring tasks, where the operator's both hands are busy, the hand-controller alone is inadequate for effective human-MRS interaction. To this end, our study takes a first step toward a multimodal interface by extending the hand-controller with a hands-free input based on gaze and brain-computer interface (BCI), i.e., a hybrid gaze-BCI. Specifically, the velocity control function is still designated to the hand-controller that excels at inputting continuous velocity commands for MRS, while the formation control function is realized with a more intuitive hybrid gaze-BCI, rather than with the hand-controller via a less natural mapping. In a dual-task experimental paradigm that simulated the hands-occupied manipulation condition in real-world applications, operators achieved improved performance for controlling simulated MRS (average formation inputting accuracy increases 3%, average finishing time decreases 5 s), reduced cognitive load (average reaction time for secondary task decreases 0.32 s) and perceived workload (average rating score decreases 15.84) with the hand-controller extended by the hybrid gaze-BCI, over those with the hand-controller alone. These findings reveal the potential of the hands-free hybrid gaze-BCI to extend the traditional manual MRS input devices for creating a more operator-friendly interface, in challenging hands-occupied dual-tasking scenarios.

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Section: Related Workmentioning

confidence: 99%

“…Harnessing the gaze to infer the operator's intended onscreen commands promises to be a natural control interface for human-MRS interactions [7], [8], [9], [10]. Nevertheless, accuracy of gaze-based input is found to largely depend on the target size [11].…”

Section: Introductionmentioning

confidence: 99%

Extended Control With Hybrid Gaze-BCI for Multi-Robot System Under Hands-Occupied Dual-Tasking

Zeng

Shen

Sun

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…In the first ten experiments (participants from #1 to #10), we used the parameters of the ball selection algorithm from the study [22], where gaze was used for selection without voice confirmation. Specifically, when a confirming action occurred, regardless of the condition, the median of the distances from the gaze position to a ball center was calculated for each ball in a window of ~867 ms length.…”

Section: Online Gaze-based Selectionmentioning

confidence: 99%

“…We suggested that it is, nevertheless, possible to find short words that could be comfortably and effectively used simply as a selection operation in gaze-based interaction. For testing this hypothesis, we added a simple, word-insensitive voice-based selection confirmation to our experimental platform ( [22], Zhao et al, under review). This platform presents experiment participants with moving objects that they should select using their smooth pursuit eye movements, which are detected using an eye tracker.…”

Section: Introductionmentioning

confidence: 99%

“…This platform presents experiment participants with moving objects that they should select using their smooth pursuit eye movements, which are detected using an eye tracker. Selection based on smooth pursuit has been developed in recent years as a powerful variant of gaze-based interaction [23][24][25][26][27][28], and it was shown that gaze-based selection can even be made faster than mouse-based selection in the case of selecting moving objects [22]. We designed an experiment, in which healthy participants were asked to make, as fast as possible, multiple selections using gaze and simple uniform voice command.…”

Section: Introductionmentioning

confidence: 99%

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Voice as a Mouse Click: Usability and Effectiveness of Simplified Hands-Free Gaze-Voice Selection

et al. 2020

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Voice- and gaze-based hands-free input are increasingly used in human-machine interaction. Attempts to combine them into a hybrid technology typically employ the voice channel as an information-rich channel. Voice seems to be “overqualified” to serve simply as a substitute of a computer mouse click, to confirm selections made by gaze. It could be expected that the user would feel discomfort if they had to frequently make “clicks” using their voice, or easily get bored, which also could lead to low performance. To test this, we asked 23 healthy participants to select moving objects with smooth pursuit eye movements. Manual confirmation of selection was faster and rated as more convenient than voice-based confirmation. However, the difference was not high, especially when voice was used to pronounce objects’ numbers (speech recognition was not applied): Score of convenience (M ± SD) was 9.2 ± 1.1 for manual and 8.0 ± 2.1 for voice confirmation, and time spent per object was 1269 ± 265 ms and 1626 ± 331 ms, respectively. We conclude that “voice-as-click” can be used to confirm selection in gaze-based interaction with computers as a substitute for the computer mouse click when manual confirmation cannot be used.

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A passive BCI for monitoring the intentionality of the gaze-based moving object selection

et al. 2021

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Objective. The use of an electroencephalogram (EEG) anticipation-related component, the expectancy wave (E-wave), in brain–machine interaction was proposed more than 50 years ago. This possibility was not explored for decades, but recently it was shown that voluntary attempts to select items using eye fixations, but not spontaneous eye fixations, are accompanied by the E-wave. Thus, the use of the E-wave detection was proposed for the enhancement of gaze interaction technology, which has a strong need for a mean to decide if a gaze behavior is voluntary or not. Here, we attempted at estimating whether this approach can be used in the context of moving object selection through smooth pursuit eye movements. Approach. Eighteen participants selected, one by one, items which moved on a computer screen, by gazing at them. In separate runs, the participants performed tasks not related to voluntary selection but also provoking smooth pursuit. A low-cost consumer-grade eye tracker was used for item selection. Main results. A component resembling the E-wave was found in the averaged EEG segments time-locked to voluntary selection events of every participant. Linear discriminant analysis with shrinkage regularization classified the intentional and spontaneous smooth pursuit eye movements, using single-trial 300 ms long EEG segments, significantly above chance in eight participants. When the classifier output was averaged over ten subsequent data segments, median group ROC AUC of 0.75 was achieved. Significance. The results suggest the possible usefulness of the E-wave detection in the gaze-based selection of moving items, e.g. in video games. This technique might be more effective when trial data can be averaged, thus it could be considered for use in passive interfaces, for example, in estimating the degree of the user’s involvement during gaze-based interaction.

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The Pursuing Gaze Beats Mouse in Non-Pop-Out Target Selection

Cited by 4 publications

References 17 publications

Extended Control With Hybrid Gaze-BCI for Multi-Robot System Under Hands-Occupied Dual-Tasking

Extended Control With Hybrid Gaze-BCI for Multi-Robot System Under Hands-Occupied Dual-Tasking

Voice as a Mouse Click: Usability and Effectiveness of Simplified Hands-Free Gaze-Voice Selection

A passive BCI for monitoring the intentionality of the gaze-based moving object selection

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