GIMIS: Gaze Input with Motor Imagery Selection

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

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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.
Original languageEnglish
Title of host publicationProceedings of ACM Symposium on Eye Tracking Research and Applications
PublisherAssociation for Computing Machinery
Publication date2020
Article number18
ISBN (Print)978-1-4503-7135-3
Publication statusPublished - 2020
EventACM Symposium on Eye Tracking Research and Applications - Stuttgart , Germany
Duration: 2 Jun 20205 Jun 2020


ConferenceACM Symposium on Eye Tracking Research and Applications


  • 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


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