Literature DB >> 19965222

An electrooculogram-based binary saccade sequence classification (BSSC) technique for augmentative communication and control.

Johnalan Keegan1, Edward Burke, James Condron.   

Abstract

In the field of assistive technology, the electrooculogram (EOG) can be used as a channel of communication and the basis of a man-machine interface. For many people with severe motor disabilities, simple actions such as changing the TV channel require assistance. This paper describes a method of detecting saccadic eye movements and the use of a saccade sequence classification algorithm to facilitate communication and control. Saccades are fast eye movements that occurs when a person's gaze jumps from one fixation point to another. The classification is based on pre-defined sequences of saccades, guided by a static visual template (e.g. a page or poster). The template, consisting of a table of symbols each having a clearly identifiable fixation point, is situated within view of the user. To execute a particular command, the user moves his or her gaze through a pre-defined path of eye movements. This results in a well-formed sequence of saccades which are translated into a command if a match is found in a library of predefined sequences. A coordinate transformation algorithm is applied to each candidate sequence of recorded saccades to mitigate the effect of changes in the user's position and orientation relative to the visual template. Upon recognition of a saccade sequence from the library, its associated command is executed. A preliminary experiment in which two subjects were instructed to perform a series of command sequences consisting of 8 different commands are presented in the final sections. The system is also shown to be extensible to facilitate convenient text entry via an alphabetic visual template.

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Year:  2009        PMID: 19965222     DOI: 10.1109/IEMBS.2009.5335325

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  3 in total

1.  Soft, conformal bioelectronics for a wireless human-wheelchair interface.

Authors:  Saswat Mishra; James J S Norton; Yongkuk Lee; Dong Sup Lee; Nicolas Agee; Yanfei Chen; Youngjae Chun; Woon-Hong Yeo
Journal:  Biosens Bioelectron       Date:  2017-01-25       Impact factor: 10.618

Review 2.  EOG-Based Human-Computer Interface: 2000-2020 Review.

Authors:  Chama Belkhiria; Atlal Boudir; Christophe Hurter; Vsevolod Peysakhovich
Journal:  Sensors (Basel)       Date:  2022-06-29       Impact factor: 3.847

3.  Volitional and Real-Time Control Cursor Based on Eye Movement Decoding Using a Linear Decoding Model.

Authors:  Jinhua Zhang; Baozeng Wang; Cheng Zhang; Jun Hong
Journal:  Comput Intell Neurosci       Date:  2016-12-13
  3 in total

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