Literature DB >> 25204774

Severely affected ALS patients have broad and high expectations for brain-machine interfaces.

Yu Kageyama1, Masayuki Hirata, Takufumi Yanagisawa, Toshio Shimokawa, Jinichi Sawada, Shayne Morris, Nozomi Mizushima, Haruhiko Kishima, Osamu Sakura, Toshiki Yoshimine.   

Abstract

Brain-machine interfaces (BMIs) may provide new communication channels and motor function to individuals with severe neurodegenerative diseases, but little is known about their interests in such devices. We investigated the interests of severely affected ALS patients in BMIs, and examined factors that might influence these interests. We conducted an anonymous, mail-back questionnaire survey of severely disabled ALS patients diagnosed using the revised El Escorial criteria. Thirty-seven patients responded to the questionnaire. Twenty-nine (78.4%) had undergone tracheostomy positive pressure ventilation. More than 80% of the patients were interested in communication support. Thirty-three (89.2%) felt stressed during communication. Among those using assistive communication devices (17 patients), 15 (88.2%) were not satisfied with them. More than 50% of the patients expressed an interest in BMIs. Their expectations of BMIs ranged widely from emergency alarm to postural change. The frequent use of personal computers tended to be correlated with an interest in invasive BMIs (p = 0.07). In conclusion, this was the first questionnaire survey demonstrating that severely affected ALS patients have broad and high expectations for BMIs. Communication was the most desired support from BMIs for such patients. We need to meet their widely ranging expectations of BMIs.

Entities:  

Keywords:  Brain-machine interfaces; amyotrophic lateral sclerosis; questionnaire survey

Mesh:

Year:  2014        PMID: 25204774     DOI: 10.3109/21678421.2014.951943

Source DB:  PubMed          Journal:  Amyotroph Lateral Scler Frontotemporal Degener        ISSN: 2167-8421            Impact factor:   4.092


  8 in total

1.  Ethical Considerations in Ending Exploratory Brain-Computer Interface Research Studies in Locked-in Syndrome.

Authors:  Eran Klein; Betts Peters; Matt Higger
Journal:  Camb Q Healthc Ethics       Date:  2018-10       Impact factor: 1.284

Review 2.  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

Review 3.  Brain-Computer Interfaces for Augmentative and Alternative Communication: A Tutorial.

Authors:  Jonathan S Brumberg; Kevin M Pitt; Alana Mantie-Kozlowski; Jeremy D Burnison
Journal:  Am J Speech Lang Pathol       Date:  2018-02-06       Impact factor: 2.408

4.  Voluntary control of semantic neural representations by imagery with conflicting visual stimulation.

Authors:  Ryohei Fukuma; Takufumi Yanagisawa; Shinji Nishimoto; Hidenori Sugano; Kentaro Tamura; Shota Yamamoto; Yasushi Iimura; Yuya Fujita; Satoru Oshino; Naoki Tani; Naoko Koide-Majima; Yukiyasu Kamitani; Haruhiko Kishima
Journal:  Commun Biol       Date:  2022-03-18

5.  Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals.

Authors:  Tomislav Milekovic; Anish A Sarma; Daniel Bacher; John D Simeral; Jad Saab; Chethan Pandarinath; Brittany L Sorice; Christine Blabe; Erin M Oakley; Kathryn R Tringale; Emad Eskandar; Sydney S Cash; Jaimie M Henderson; Krishna V Shenoy; John P Donoghue; Leigh R Hochberg
Journal:  J Neurophysiol       Date:  2018-04-25       Impact factor: 2.714

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

Review 7.  Communication Matters-Pitfalls and Promise of Hightech Communication Devices in Palliative Care of Severely Physically Disabled Patients With Amyotrophic Lateral Sclerosis.

Authors:  Katharina Linse; Elisa Aust; Markus Joos; Andreas Hermann
Journal:  Front Neurol       Date:  2018-07-27       Impact factor: 4.003

8.  A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent.

Authors:  Nicholas D Skomrock; Michael A Schwemmer; Jordyn E Ting; Hemang R Trivedi; Gaurav Sharma; Marcia A Bockbrader; David A Friedenberg
Journal:  Front Neurosci       Date:  2018-10-24       Impact factor: 4.677

  8 in total

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