Literature DB >> 23923692

A review of EEG-based brain-computer interfaces as access pathways for individuals with severe disabilities.

Saba Moghimi1, Azadeh Kushki, Anne Marie Guerguerian, Tom Chau.   

Abstract

Electroencephalography (EEG) is a non-invasive method for measuring brain activity and is a strong candidate for brain-computer interface (BCI) development. While BCIs can be used as a means of communication for individuals with severe disabilities, the majority of existing studies have reported BCI evaluations by able-bodied individuals. Considering the many differences in body functions and usage scenarios between individuals with disabilities and able-bodied individuals, involvement of the target population in BCI evaluation is necessary. In this review, 39 studies reporting EEG-oriented BCI assessment by individuals with disabilities were identified in the past decade. With respect to participant populations, a need for assessing BCI performance for the pediatric population with severe disabilities was identified as an important future direction. Acquiring a reliable communication pathway during early stages of development is crucial in avoiding learned helplessness in pediatric-onset disabilities. With respect to evaluation, augmenting traditional measures of system performance with those relating to contextual factors was recommended for realizing user-centered designs appropriate for integration in real-life. Considering indicators of user state and developing more effective training paradigms are recommended for future studies of BCI involving individuals with disabilities.

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Year:  2013        PMID: 23923692     DOI: 10.1080/10400435.2012.723298

Source DB:  PubMed          Journal:  Assist Technol        ISSN: 1040-0435


  28 in total

1.  An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling.

Authors:  Mohammad Moghadamfalahi; Murat Akcakaya; Hooman Nezamfar; Jamshid Sourati; Deniz Erdogmus
Journal:  IEEE Trans Signal Process       Date:  2017-07-17       Impact factor: 4.931

2.  Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study.

Authors:  B O Mainsah; L M Collins; K A Colwell; E W Sellers; D B Ryan; K Caves; C S Throckmorton
Journal:  J Neural Eng       Date:  2015-01-14       Impact factor: 5.379

3.  Using the detectability index to predict P300 speller performance.

Authors:  B O Mainsah; L M Collins; C S Throckmorton
Journal:  J Neural Eng       Date:  2016-10-05       Impact factor: 5.379

4.  Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction.

Authors:  B O Mainsah; G Reeves; L M Collins; C S Throckmorton
Journal:  J Neural Eng       Date:  2017-08       Impact factor: 5.379

5.  Considering Augmentative and Alternative Communication Research for Brain-Computer Interface Practice.

Authors:  Kevin M Pitt; Jonathan S Brumberg; Adrienne R Pitt
Journal:  Assist Technol Outcomes Benefits       Date:  2019

6.  Future directions for research in autism spectrum disorders.

Authors:  Cara R Damiano; Carla A Mazefsky; Susan W White; Gabriel S Dichter
Journal:  J Clin Child Adolesc Psychol       Date:  2014

7.  The Promise of Neurotechnology in Clinical Translational Science.

Authors:  Susan W White; John A Richey; Denis Gracanin; Martha Ann Bell; Stephen LaConte; Marika Coffman; Andrea Trubanova; Inyoung Kim
Journal:  Clin Psychol Sci       Date:  2014-10-17

8.  Classification of activity engagement in individuals with severe physical disabilities using signals of the peripheral nervous system.

Authors:  Azadeh Kushki; Alexander J Andrews; Sarah D Power; Gillian King; Tom Chau
Journal:  PLoS One       Date:  2012-02-17       Impact factor: 3.240

9.  Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation.

Authors:  Michael J Young; David J Lin; Leigh R Hochberg
Journal:  Semin Neurol       Date:  2021-03-19       Impact factor: 3.212

Review 10.  The Human Factors and Ergonomics of P300-Based Brain-Computer Interfaces.

Authors:  J Clark Powers; Kateryna Bieliaieva; Shuohao Wu; Chang S Nam
Journal:  Brain Sci       Date:  2015-08-10
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