What You Expect Is What You Get? Potential Use of Contingent Negative Variation for Passive BCI Systems in Gaze-Based HCI

Ihme, Klas; Zander, Thorsten O.

doi:10.1007/978-3-642-24571-8_57

Cited by 12 publications

(18 citation statements)

References 23 publications

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“…In particular, Finke et al (2016) reported high performance, with area under ROC curve (AUC) of about 0.9, for classification of short target vs. non-target fixations. Similar phenomena could be a basis of classification in studies by Ihme and Zander (2011) and Protzak et al (2013). This approach, however, is limited, because a click on a screen button or link is not always required immediately after they are found.…”

Section: Introductionmentioning

confidence: 52%

“…Zander and colleagues (Ihme and Zander, 2011; Protzak et al, 2013) proposed to make use of the EEG patterns that naturally accompany gaze based control. Their works were based on the “passive BCI” approach, i.e., the detection of brain signal patterns that naturally accompany the brain activities of interest, without the need from the user to do anything voluntarily to evoke these patterns (Zander and Kothe, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…The passive BCI approach seems to be exactly what is needed for detecting intentions in the most unobtrusive way, while gaze dwells effectively reveal the time intervals where the markers of intentions can be searched for in the brain signals. The EEG was collected during fixations intentionally used for control and during non-controlling fixations and successfully classified off-line (Ihme and Zander, 2011; Protzak et al, 2013). However, gaze dwell time thresholds were again quite long, 2000 ms in Ihme and Zander (2011) and 1000 ms in Protzak et al (2013).…”

Section: Introductionmentioning

confidence: 99%

“…The EEG was collected during fixations intentionally used for control and during non-controlling fixations and successfully classified off-line (Ihme and Zander, 2011; Protzak et al, 2013). However, gaze dwell time thresholds were again quite long, 2000 ms in Ihme and Zander (2011) and 1000 ms in Protzak et al (2013). Moreover, the participant task in both studies involved visual search, and the classification could rely mainly on fixation-related potential (FRP) components related to finding a target, rather than to intention to act.…”

Section: Introductionmentioning

confidence: 99%

“…The current study followed the proposals of Zander and colleagues (Ihme and Zander, 2011; Protzak et al, 2013), i.e., it relied on finding EEG (FRP) markers for gaze dwells intentionally used for control. However, we aimed on investigating the possibility to find the FRP markers of controlling fixations when relatively short (500 ms) dwell time threshold is used.…”

Section: Introductionmentioning

confidence: 99%

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EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

et al. 2016

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We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG) marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs) were collected during game's control-on and control-off conditions. A slow negative wave with a maximum in the parietooccipital region was present in each participant's averaged FRPs in the control-on conditions and was absent or had much lower amplitude in the control-off condition. This wave was similar but not identical to stimulus-preceding negativity, a slow negative wave that can be observed during feedback expectation. Classification of intentional vs. spontaneous fixations was based on amplitude features from 13 EEG channels using 300 ms length segments free from electrooculogram contamination (200–500 ms relative to the fixation onset). For the first fixations in the fixation triplets required to make moves in the game, classified against control-off data, a committee of greedy classifiers provided 0.90 ± 0.07 specificity and 0.38 ± 0.14 sensitivity. Similar (slightly lower) results were obtained for the shrinkage Linear Discriminate Analysis (LDA) classifier. The second and third fixations in the triplets were classified at lower rate. We expect that, with improved feature sets and classifiers, a hybrid dwell-based Eye-Brain-Computer Interface (EBCI) can be built using the FRP difference between the intended and spontaneous fixations. If this direction of BCI development will be successful, such a multimodal interface may improve the fluency of interaction and can possibly become the basis for a new input device for paralyzed and healthy users, the EBCI “Wish Mouse.”

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Section: Introductionmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

et al. 2016

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A Passive Brain-Computer Interface for Supporting Gaze-Based Human-Machine Interaction

Protzak

Ihme

Zander

2013

Lecture Notes in Computer Science

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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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What You Expect Is What You Get? Potential Use of Contingent Negative Variation for Passive BCI Systems in Gaze-Based HCI

Cited by 12 publications

References 23 publications

EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

A Passive Brain-Computer Interface for Supporting Gaze-Based Human-Machine Interaction

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

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