Literature DB >> 30368637

EEG-EOG based Virtual Keyboard: Toward Hybrid Brain Computer Interface.

Sarah M Hosni1, Howida A Shedeed1, Mai S Mabrouk2, Mohamed F Tolba1.   

Abstract

The past twenty years have ignited a new spark in the research of Electroencephalogram (EEG), which was pursued to develop innovative Brain Computer Interfaces (BCIs) in order to help severely disabled people live a better life with a high degree of independence. Current BCIs are more theoretical than practical and are suffering from numerous challenges. New trends of research propose combining EEG to other simple and efficient bioelectric inputs such as Electro-oculography (EOG) resulting from eye movements, to produce more practical and robust Hybrid Brain Computer Interface systems (hBCI) or Brain/Neuronal Computer Interface (BNCI). Working towards this purpose, existing research in EOG based Human Computer Interaction (HCI) applications, must be organized and surveyed in order to develop a vision on the potential benefits of combining both input modalities and give rise to new designs that maximize these benefits. Our aim is to support and inspire the design of new hBCI systems based on both EEG and EOG signals, in doing so; first the current EOG based HCI systems were surveyed with a particular focus on EOG based systems for communication using virtual keyboard. Then, a survey of the current EEG-EOG virtual keyboard was performed highlighting the design protocols employed. We concluded with a discussion of the potential advantages of combining both systems with recommendations to give deep insight for future design issues for all EEG-EOG hBCI systems. Finally, a general architecture was proposed for a new EEG-EOG hBCI system. The proposed hybrid system completely alters the traditional view of the eye movement features present in EEG signal as artifacts that should be removed; instead EOG traces are extracted from EEG in our proposed hybrid architecture and are considered as an additional input modality sharing control according to the chosen design protocol.

Entities:  

Keywords:  Brain/Neuronal Computer Interface; Electroencephalogram; Electrooculography; Hybrid Brain-Computer Interface; Virtual Keyboard

Mesh:

Year:  2019        PMID: 30368637     DOI: 10.1007/s12021-018-9402-0

Source DB:  PubMed          Journal:  Neuroinformatics        ISSN: 1539-2791


  32 in total

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Authors:  G Townsend; B K LaPallo; C B Boulay; D J Krusienski; G E Frye; C K Hauser; N E Schwartz; T M Vaughan; J R Wolpaw; E W Sellers
Journal:  Clin Neurophysiol       Date:  2010-03-26       Impact factor: 3.708

4.  Hybrid EEG-EOG brain-computer interface system for practical machine control.

Authors:  Yunyong Punsawad; Yodchanan Wongsawat; Manukid Parnichkun
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2010

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Authors:  Kenji Yamagishi; Junichi Hori; Michio Miyakawa
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2006

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Authors:  Rafael Barea; Luciano Boquete; Manuel Mazo; Elena López
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2002-12       Impact factor: 3.802

7.  A hybrid platform based on EOG and EEG signals to restore communication for patients afflicted with progressive motor neuron diseases.

Authors:  A B Usakli; S Gurkan; F Aloise; G Vecchiato; F Babiloni
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2009

8.  Electrooculogram based system for computer control using a multiple feature classification model.

Authors:  Armen R Kherlopian; Joseph P Gerrein; Minerva Yue; Kristina E Kim; Ji Won Kim; Madhav Sukumaran; Paul Sajda
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2006

9.  A low-cost interface for control of computer functions by means of eye movements.

Authors:  D Borghetti; A Bruni; M Fabbrini; L Murri; F Sartucci
Journal:  Comput Biol Med       Date:  2007-06-28       Impact factor: 4.589

10.  ISCEV Standard for Clinical Electro-oculography (EOG) 2006.

Authors:  Malcolm Brown; Michael Marmor; Eberhard Zrenner; Mitchell Brigell; Michael Bach
Journal:  Doc Ophthalmol       Date:  2006-11-16       Impact factor: 2.379

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