Literature DB >> 16341930

A general framework for characterizing studies of brain interface technology.

S G Mason1, M M Moore Jackson, G E Birch.   

Abstract

The development of brain interface (BI) technology continues to attract researchers with a wide range of backgrounds and expertise. Though the BI community is committed to accurate and objective evaluation of methods, systems, and technology, the very diversity of the methods and terminology used in the field hinders understanding and impairs technology cross-fertilization and cross-group validation of findings. Underlying this dilemma is a lack of common perspective and language. As seen in our previous works in this area, our approach to remedy this problem is to propose language in the form of taxonomy and functional models. Our intent is to document and validate our best thinking in this area and publish a perspective that will stimulate discussion. We encourage others to do the same with the belief that focused discussion on language issues will accelerate the inherently slow natural evolution of language selection and thus alleviate related problems. In this work, we propose a theoretical framework for describing BI-technology-related studies. The proposed framework is based on the theoretical concepts and terminology from classical science, assistive technology development, human-computer interaction, and previous BI-related works. Using a representative set of studies from the literature, the proposed BI study framework was shown to be complete and appropriate perspective for thoroughly characterizing a BI study. We have also demonstrated that this BI study framework is useful for (1) objectively reviewing existing BI study designs and results, (2) comparing designs and results of multiple BI studies, (3) designing new studies or objectively reporting BI study results, and (4) facilitating intra- and inter-group communication and the education of new researchers. As such, it forms a sound and appropriate basis for community discussion.

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Year:  2005        PMID: 16341930     DOI: 10.1007/s10439-005-7706-3

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  4 in total

1.  Describing different brain computer interface systems through a unique model: a UML implementation.

Authors:  Lucia Rita Quitadamo; Maria Grazia Marciani; Gian Carlo Cardarilli; Luigi Bianchi
Journal:  Neuroinformatics       Date:  2008-07-08

2.  Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation.

Authors:  Jane E Huggins; Christoph Guger; Erik Aarnoutse; Brendan Allison; Charles W Anderson; Steven Bedrick; Walter Besio; Ricardo Chavarriaga; Jennifer L Collinger; An H Do; Christian Herff; Matthias Hohmann; Michelle Kinsella; Kyuhwa Lee; Fabien Lotte; Gernot Müller-Putz; Anton Nijholt; Elmar Pels; Betts Peters; Felix Putze; Rüdiger Rupp; Gerwin Schalk; Stephanie Scott; Michael Tangermann; Paul Tubig; Thorsten Zander
Journal:  Brain Comput Interfaces (Abingdon)       Date:  2019-12-10

3.  Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors.

Authors:  Lun-De Liao; Chi-Yu Chen; I-Jan Wang; Sheng-Fu Chen; Shih-Yu Li; Bo-Wei Chen; Jyh-Yeong Chang; Chin-Teng Lin
Journal:  J Neuroeng Rehabil       Date:  2012-01-28       Impact factor: 4.262

4.  Emerging Therapeutic Enhancement Enabling Health Technologies and Their Discourses: What Is Discussed within the Health Domain?

Authors:  Gregor Wolbring; Lucy Diep; Sophya Yumakulov; Natalie Ball; Verlyn Leopatra; Dean Yergens
Journal:  Healthcare (Basel)       Date:  2013-07-25
  4 in total

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