Evidence map›Paper›PMID 36068570›Full record

ArticleJournal of neuroengineering and rehabilitation2022

Competing at the Cybathlon championship for people with disabilities: long-term motor imagery brain-computer interface training of a cybathlete who has tetraplegia.

Attila Korik, Karl McCreadie, Niall McShane, Naomi Du Bois, Massoud Khodadadzadeh, Jacqui Stow, Jacinta McElligott, Áine Carroll, Damien Coyle

Open access · goldAbstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 16% of its field
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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
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

9 authors at 3 institutions in 2 countries.

Attila KorikIntelligent Systems Research Centre, Ulster University, Derry, UK. a.korik@ulster.ac.uk.ORCID 0000-0002-3757-6092
Karl McCreadieIntelligent Systems Research Centre, Ulster University, Derry, UK.
Niall McShaneIntelligent Systems Research Centre, Ulster University, Derry, UK.
Naomi Du BoisIntelligent Systems Research Centre, Ulster University, Derry, UK.
Massoud KhodadadzadehIntelligent Systems Research Centre, Ulster University, Derry, UK.
Jacqui StowNational Rehabilitation Hospital of Ireland, Dun Laoghaire, Ireland.
Jacinta McElligottNational Rehabilitation Hospital of Ireland, Dun Laoghaire, Ireland.
Áine CarrollNational Rehabilitation Hospital of Ireland, Dun Laoghaire, Ireland.
Damien CoyleIntelligent Systems Research Centre, Ulster University, Derry, UK.
University of Ulster · GBNational Rehabilitation Hospital · IEUniversity College Dublin · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe brain-computer interface (BCI) race at the Cybathlon championship, for people with disabilities, challenges teams (BCI researchers, developers and pilots with spinal cord injury) to control an avatar on a virtual racetrack without movement. Here we describe the training regime and results of the Ulster University BCI Team pilot who has tetraplegia and was trained to use an electroencephalography (EEG)-based BCI intermittently over 10 years, to compete in three Cybathlon events.

methodsA multi-class, multiple binary classifier framework was used to decode three kinesthetically imagined movements (motor imagery of left arm, right arm, and feet), and relaxed state. Three game paradigms were used for training i.e., NeuroSensi, Triad, and Cybathlon Race: BrainDriver. An evaluation of the pilot's performance is presented for two Cybathlon competition training periods-spanning 20 sessions over 5 weeks prior to the 2019 competition, and 25 sessions over 5 weeks in the run up to the 2020 competition.

resultsHaving participated in BCI training in 2009 and competed in Cybathlon 2016, the experienced pilot achieved high two-class accuracy on all class pairs when training began in 2019 (decoding accuracy > 90%, resulting in efficient NeuroSensi and Triad game control). The BrainDriver performance (i.e., Cybathlon race completion time) improved significantly during the training period, leading up to the competition day, ranging from 274-156 s (255 ± 24 s to 191 ± 14 s mean ± std), over 17 days (10 sessions) in 2019, and from 230-168 s (214 ± 14 s to 181 ± 4 s), over 18 days (13 sessions) in 2020. However, on both competition occasions, towards the race date, the performance deteriorated significantly.

conclusionsThe training regime and framework applied were highly effective in achieving competitive race completion times. The BCI framework did not cope with significant deviation in electroencephalography (EEG) observed in the sessions occurring shortly before and during the race day. Changes in cognitive state as a result of stress, arousal level, and fatigue, associated with the competition challenge and performance pressure, were likely contributing factors to the non-stationary effects that resulted in the BCI and pilot achieving suboptimal performance on race day. Trial registration not registered.

Indexed as

Brain-Computer InterfacesPersons with DisabilitiesElectroencephalographyHumansImagery, PsychotherapyQuadriplegiaAlternative and augmentative and assistive communication (AAC) deviceBrain–computer interface (BCI)CompetitionElectroencephalography (EEG)Long-term trainingMotor imageryNeurofeedbackNeurogamingTetraplegia

Identifiers

PMID36068570
PMCPMC9446658
OpenAlexW4294919628

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.