Literature DB >> 26169755

Invasive brain-machine interfaces: a survey of paralyzed patients' attitudes, knowledge and methods of information retrieval.

Jacob Lahr1, Christina Schwartz, Bernhard Heimbach, Ad Aertsen, Jörn Rickert, Tonio Ball.   

Abstract

OBJECTIVE: Brain-machine interfaces (BMI) are an emerging therapeutic option that can allow paralyzed patients to gain control over assistive technology devices (ATDs). BMI approaches can be broadly classified into invasive (based on intracranially implanted electrodes) and noninvasive (based on skin electrodes or extracorporeal sensors). Invasive BMIs have a favorable signal-to-noise ratio, and thus allow for the extraction of more information than noninvasive BMIs, but they are also associated with the risks related to neurosurgical device implantation. Current noninvasive BMI approaches are typically concerned, among other issues, with long setup times and/or intensive training. Recent studies have investigated the attitudes of paralyzed patients eligible for BMIs, particularly patients affected by amyotrophic lateral sclerosis (ALS). These studies indicate that paralyzed patients are indeed interested in BMIs. Little is known, however, about the degree of knowledge among paralyzed patients concerning BMI approaches or about how patients retrieve information on ATDs. Furthermore, it is not yet clear if paralyzed patients would accept intracranial implantation of BMI electrodes with the premise of decoding improvements, and what the attitudes of a broader range of patients with diseases such as stroke or spinal cord injury are towards this new kind of treatment. APPROACH: Using a questionnaire, we surveyed 131 paralyzed patients for their opinions on invasive BMIs and their attitude toward invasive BMI treatment options. MAIN
RESULTS: The majority of the patients knew about and had a positive attitude toward invasive BMI approaches. The group of ALS patients was especially open to the concept of BMIs. The acceptance of invasive BMI technology depended on the improvements expected from the technology. Furthermore, the survey revealed that for paralyzed patients, the Internet is an important source of information on ATDs. SIGNIFICANCE: Websites tailored to prospective BMI users should be further developed to provide reliable information to patients, and also to help to link prospective BMI users with researchers involved in the development of BMI technology.

Entities:  

Mesh:

Year:  2015        PMID: 26169755     DOI: 10.1088/1741-2560/12/4/043001

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  8 in total

Review 1.  Review: Human Intracortical Recording and Neural Decoding for Brain-Computer Interfaces.

Authors:  David M Brandman; Sydney S Cash; Leigh R Hochberg
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2017-03-02       Impact factor: 3.802

2.  Brain-Computer interfaces for communication: preferences of individuals with locked-in syndrome, caregivers and researchers.

Authors:  Mariana P Branco; Elmar G M Pels; Femke Nijboer; Nick F Ramsey; Mariska J Vansteensel
Journal:  Disabil Rehabil Assist Technol       Date:  2021-08-12

3.  Decoding four hand gestures with a single bipolar pair of electrocorticography electrodes.

Authors:  Maxime Verwoert; Mariska J Vansteensel; Zachary V Freudenburg; Erik J Aarnoutse; Frans S S Leijten; Nick F Ramsey; Mariana P Branco
Journal:  J Neural Eng       Date:  2021-10-22       Impact factor: 5.043

4.  EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface.

Authors:  Sergei L Shishkin; Yuri O Nuzhdin; Evgeny P Svirin; Alexander G Trofimov; Anastasia A Fedorova; Bogdan L Kozyrskiy; Boris M Velichkovsky
Journal:  Front Neurosci       Date:  2016-11-18       Impact factor: 4.677

5.  Signal quality of simultaneously recorded endovascular, subdural and epidural signals are comparable.

Authors:  Sam E John; Nicholas L Opie; Yan T Wong; Gil S Rind; Stephen M Ronayne; Giulia Gerboni; Sebastien H Bauquier; Terence J O'Brien; Clive N May; David B Grayden; Thomas J Oxley
Journal:  Sci Rep       Date:  2018-05-30       Impact factor: 4.379

6.  Using brain-computer interfaces: a scoping review of studies employing social research methods.

Authors:  Johannes Kögel; Jennifer R Schmid; Ralf J Jox; Orsolya Friedrich
Journal:  BMC Med Ethics       Date:  2019-03-07       Impact factor: 2.652

7.  Brain-Computer Interfaces for Communication: Preferences of Individuals With Locked-in Syndrome.

Authors:  Mariana P Branco; Elmar G M Pels; Ruben H Sars; Erik J Aarnoutse; Nick F Ramsey; Mariska J Vansteensel; Femke Nijboer
Journal:  Neurorehabil Neural Repair       Date:  2021-02-03       Impact factor: 3.919

8.  Defining Surgical Terminology and Risk for Brain Computer Interface Technologies.

Authors:  Eric C Leuthardt; Daniel W Moran; Tim R Mullen
Journal:  Front Neurosci       Date:  2021-03-26       Impact factor: 4.677

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.