Literature DB >> 29024794

Brain computer interface with the P300 speller: Usability for disabled people with amyotrophic lateral sclerosis.

Violaine Guy1, Marie-Hélène Soriani1, Mariane Bruno1, Théodore Papadopoulo2, Claude Desnuelle3, Maureen Clerc2.   

Abstract

OBJECTIVES: Amyotrophic lateral sclerosis (ALS), a progressive neurodegenerative disease, restricts patients' communication capacity a few years after onset. A proof-of-concept of brain-computer interface (BCI) has shown promise in ALS and "locked-in" patients, mostly in pre-clinical studies or with only a few patients, but performance was estimated not high enough to support adoption by people with physical limitation of speech. Here, we evaluated a visual BCI device in a clinical study to determine whether disabled people with multiple deficiencies related to ALS would be able to use BCI to communicate in a daily environment.
METHODS: After clinical evaluation of physical, cognitive and language capacities, 20 patients with ALS were included. The P300 speller BCI system consisted of electroencephalography acquisition connected to real-time processing software and separate keyboard-display control software. It was equipped with original features such as optimal stopping of flashes and word prediction. The study consisted of two 3-block sessions (copy spelling, free spelling and free use) with the system in several modes of operation to evaluate its usability in terms of effectiveness, efficiency and satisfaction.
RESULTS: The system was effective in that all participants successfully achieved all spelling tasks and was efficient in that 65% of participants selected more than 95% of the correct symbols. The mean number of correct symbols selected per minute ranged from 3.6 (without word prediction) to 5.04 (with word prediction). Participants expressed satisfaction: the mean score was 8.7 on a 10-point visual analog scale assessing comfort, ease of use and utility. Patients quickly learned how to operate the system, which did not require much learning effort.
CONCLUSION: With its word prediction and optimal stopping of flashes, which improves information transfer rate, the BCI system may be competitive with alternative communication systems such as eye-trackers. Remaining requirements to improve the device for suitable ergonomic use are in progress.
Copyright © 2017. Published by Elsevier Masson SAS.

Entities:  

Keywords:  Amyotrophic lateral sclerosis; Augmentative and alternative communication; Brain–computer interface; P300 speller

Mesh:

Year:  2017        PMID: 29024794     DOI: 10.1016/j.rehab.2017.09.004

Source DB:  PubMed          Journal:  Ann Phys Rehabil Med        ISSN: 1877-0657


  20 in total

Review 1.  Progress in Brain Computer Interface: Challenges and Opportunities.

Authors:  Simanto Saha; Khondaker A Mamun; Khawza Ahmed; Raqibul Mostafa; Ganesh R Naik; Sam Darvishi; Ahsan H Khandoker; Mathias Baumert
Journal:  Front Syst Neurosci       Date:  2021-02-25

2.  Usability of a Hybrid System Combining P300-Based Brain-Computer Interface and Commercial Assistive Technologies to Enhance Communication in People With Multiple Sclerosis.

Authors:  Angela Riccio; Francesca Schettini; Valentina Galiotta; Enrico Giraldi; Maria Grazia Grasso; Febo Cincotti; Donatella Mattia
Journal:  Front Hum Neurosci       Date:  2022-05-26       Impact factor: 3.473

3.  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

4.  Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface.

Authors:  Shi Qiu; Junjun Li; Mengdi Cong; Chun Wu; Yan Qin; Ting Liang
Journal:  Comput Math Methods Med       Date:  2020-06-15       Impact factor: 2.238

Review 5.  Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review.

Authors:  Lucas Medeiros Souza do Nascimento; Lucas Vacilotto Bonfati; Melissa La Banca Freitas; José Jair Alves Mendes Junior; Hugo Valadares Siqueira; Sergio Luiz Stevan
Journal:  Sensors (Basel)       Date:  2020-07-22       Impact factor: 3.576

6.  Dynamic time window mechanism for time synchronous VEP-based BCIs-Performance evaluation with a dictionary-supported BCI speller employing SSVEP and c-VEP.

Authors:  Felix Gembler; Piotr Stawicki; Abdul Saboor; Ivan Volosyak
Journal:  PLoS One       Date:  2019-06-13       Impact factor: 3.240

7.  Effects of Active Upper Limb Orthoses Using Brain-Machine Interfaces for Rehabilitation of Patients With Neurological Disorders: Protocol for a Systematic Review and Meta-Analysis.

Authors:  Emília M G S Silva; Ledycnarf J Holanda; Gustavo K B Coutinho; Fernanda S Andrade; Gabriel I S Nascimento; Danilo A P Nagem; Ricardo A de M Valentim; Ana Raquel Lindquist
Journal:  Front Neurosci       Date:  2021-06-24       Impact factor: 4.677

8.  Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network.

Authors:  Jaehong Yoon; Jungnyun Lee; Mincheol Whang
Journal:  Comput Intell Neurosci       Date:  2018-05-15

Review 9.  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

10.  Electroencephalography-based endogenous brain-computer interface for online communication with a completely locked-in patient.

Authors:  Chang-Hee Han; Yong-Wook Kim; Do Yeon Kim; Seung Hyun Kim; Zoran Nenadic; Chang-Hwan Im
Journal:  J Neuroeng Rehabil       Date:  2019-01-30       Impact factor: 4.262

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