Evidence map›Paper›PMID 28379187›Full record

ArticleBrain sciences2017

A Novel Hybrid Mental Spelling Application Based on Eye Tracking and SSVEP-Based BCI.

Piotr Stawicki, Felix Gembler, Aya Rezeika, Ivan Volosyak

Abstract read
In one paragraph

Article in Brain sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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  14. Robust Fusion of c-VEP and Gaze.IEEE sensors letters · 2019
    Article
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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

4 authors.

Piotr StawickiFaculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany. piotr.stawicki@hochschule-rhein-waal.de.
Felix GemblerFaculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany. felix.gembler@hochschule-rhein-waal.de.
Aya RezeikaFaculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany. aya.rezeika@hsrw.org.
Ivan VolosyakFaculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany. ivan.volosyak@hochschule-rhein-waal.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Steady state visual evoked potentials (SSVEPs)-based Brain-Computer interfaces (BCIs), as well as eyetracking devices, provide a pathway for re-establishing communication for people with severe disabilities. We fused these control techniques into a novel eyetracking/SSVEP hybrid system, which utilizes eye tracking for initial rough selection and the SSVEP technology for fine target activation. Based on our previous studies, only four stimuli were used for the SSVEP aspect, granting sufficient control for most BCI users. As Eye tracking data is not used for activation of letters, false positives due to inappropriate dwell times are avoided. This novel approach combines the high speed of eye tracking systems and the high classification accuracies of low target SSVEP-based BCIs, leading to an optimal combination of both methods. We evaluated accuracy and speed of the proposed hybrid system with a 30-target spelling application implementing all three control approaches (pure eye tracking, SSVEP and the hybrid system) with 32 participants. Although the highest information transfer rates (ITRs) were achieved with pure eye tracking, a considerable amount of subjects was not able to gain sufficient control over the stand-alone eye-tracking device or the pure SSVEP system (78.13% and 75% of the participants reached reliable control, respectively). In this respect, the proposed hybrid was most universal (over 90% of users achieved reliable control), and outperformed the pure SSVEP system in terms of speed and user friendliness. The presented hybrid system might offer communication to a wider range of users in comparison to the standard techniques.

Indexed as

Brain-Computer Interface (BCI)Electroencephalogram (EEG)Eye TrackingHybrid BCISteady State Visual Evoked Potential (SSVEP)

Identifiers

PMID28379187
PMCPMC5406692

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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.