Evidence map›Paper›PMID 27917105›Full record

ArticleFrontiers in neuroscience2016

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

Sergei L Shishkin, Yuri O Nuzhdin, Evgeny P Svirin, Alexander G Trofimov, Anastasia A Fedorova, Bogdan L Kozyrskiy, Boris M Velichkovsky

Abstract read
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Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sergei L ShishkinDepartment of Neurocognitive Technologies, Kurchatov Complex of NBICS Technologies, National Research Centre "Kurchatov Institute," Moscow, Russia.
Yuri O NuzhdinDepartment of Neurocognitive Technologies, Kurchatov Complex of NBICS Technologies, National Research Centre "Kurchatov Institute," Moscow, Russia.
Evgeny P SvirinDepartment of Neurocognitive Technologies, Kurchatov Complex of NBICS Technologies, National Research Centre "Kurchatov Institute," Moscow, Russia.
Alexander G TrofimovDepartment of Cybernetics, National Research Nuclear University MEPhI Moscow, Russia.
Anastasia A FedorovaDepartment of Neurocognitive Technologies, Kurchatov Complex of NBICS Technologies, National Research Centre "Kurchatov Institute," Moscow, Russia.
Bogdan L KozyrskiyDepartment of Neurocognitive Technologies, Kurchatov Complex of NBICS Technologies, National Research Centre "Kurchatov Institute,"Moscow, Russia; Department of Computer Systems and Technologies, National Research Nuclear University MEPhIMoscow, Russia.
Boris M VelichkovskyDepartment of Neurocognitive Technologies, Kurchatov Complex of NBICS Technologies, National Research Centre "Kurchatov Institute,"Moscow, Russia; Centre for Cognitive Programs and Technologies, Russian State University for HumanitiesMoscow, Russia; Department of Psychology, Technische Universität DresdenDresden, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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."

Indexed as

assistive technologybrain-computer interfacesdetection of intentioneye trackinggaze interactionhuman-computer interfacesslow cortical potentialsstimulus-preceding negativity

Identifiers

PMID27917105
PMCPMC5114310

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.