GIMIS: Gaze Input with Motor Imagery Selection

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GIMIS: Gaze Input with Motor Imagery Selection

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Hou, Baosheng James / Bekgaard, Per / MacKenzie, I. Scott / Hansen, John Paulin / Puthusserypady, Sadasivan

A hybrid gaze and brain-computer interface (BCI) was developed to accomplish target selection in a Fitts’ law experiment. The method, GIMIS, uses gaze input to steer the computer cursor for target pointing and motor imagery (MI) via the BCI to execute a click for target selection. An experiment (n = 15) compared three motor imagery selection methods: using the left-hand only, using the legs, and using either the left-hand or legs. The latter selection method (”either”) had the highest throughput (0.59 bps), the fastest selection time (2650 ms), and an error rate of 14.6%. Pupil size significantly increased with increased target width. We recommend the use of large targets, which significantly reduced error rate, and the ”either” option for BCI selection, which significantly increased throughput. BCI selection is slower compared to dwell time selection, but if gaze control is deteriorating, for example in a late stage of the ALS disease, GIMIS may be a way to gradually introduce BCI.

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Dokumentinformationen

Titel :

GIMIS: Gaze Input with Motor Imagery Selection

Beteiligte:

Hou, Baosheng James (Autor:in) / Bekgaard, Per (Autor:in) / MacKenzie, I. Scott (Autor:in) / Hansen, John Paulin (Autor:in) / Puthusserypady, Sadasivan (Autor:in)

Erscheinungsdatum :

2020-01-01

Anmerkungen:

Hou , B J , Bekgaard , P , MacKenzie , I S , Hansen , J P & Puthusserypady , S 2020 , GIMIS: Gaze Input with Motor Imagery Selection . in Proceedings of ACM Symposium on Eye Tracking Research and Applications . , 18 , Association for Computing Machinery , pp. 1-10 , ACM Symposium on Eye Tracking Research and Applications , Stuttgart , Baden-Württemberg , Germany , 02/06/2020 . https://doi.org/10.1145/3379157.3388932

DOI :

https://doi.org/10.1145/3379157.3388932

Medientyp :

Aufsatz (Zeitschrift)

Format :

Elektronische Ressource

Sprache :

Englisch

Schlagwörter :

Fitts’ law , Brain-computer interaction , Gaze interaction , Hybrid BCI , Amyotrophic lateral sclerosis Amyotrophic Lateral Sclerosis (MeSH) nervous system disease , muscle disease pathology , Augmentative and alternative communication , Pupillometry , Pupil size