Literature DB >> 26824590

Performance predictors of brain-computer interfaces in patients with amyotrophic lateral sclerosis.

A Geronimo1, Z Simmons, S J Schiff.   

Abstract

OBJECTIVE: Patients with amyotrophic lateral sclerosis (ALS) may benefit from brain-computer interfaces (BCI), but the utility of such devices likely will have to account for the functional, cognitive, and behavioral heterogeneity of this neurodegenerative disorder. APPROACH: In this study, a heterogeneous group of patients with ALS participated in a study on BCI based on the P300 event related potential and motor-imagery.
RESULTS: The presence of cognitive impairment in these patients significantly reduced the quality of the control signals required to use these communication systems, subsequently impairing performance, regardless of progression of physical symptoms. Loss in performance among the cognitively impaired was accompanied by a decrease in the signal-to-noise ratio of task-relevant EEG band power. There was also evidence that behavioral dysfunction negatively affects P300 speller performance. Finally, older participants achieved better performance on the P300 system than the motor-imagery system, indicating a preference of BCI paradigm with age. SIGNIFICANCE: These findings highlight the importance of considering the heterogeneity of disease when designing BCI augmentative and alternative communication devices for clinical applications.

Entities:  

Mesh:

Year:  2016        PMID: 26824590     DOI: 10.1088/1741-2560/13/2/026002

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


  15 in total

Review 1.  Guidelines for Feature Matching Assessment of Brain-Computer Interfaces for Augmentative and Alternative Communication.

Authors:  Kevin M Pitt; Jonathan S Brumberg
Journal:  Am J Speech Lang Pathol       Date:  2018-08-06       Impact factor: 2.408

Review 2.  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

3.  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

4.  Examining sensory ability, feature matching and assessment-based adaptation for a brain-computer interface using the steady-state visually evoked potential.

Authors:  Jonathan S Brumberg; Anh Nguyen; Kevin M Pitt; Sean D Lorenz
Journal:  Disabil Rehabil Assist Technol       Date:  2018-01-31

5.  Behind the Scenes of Noninvasive Brain-Computer Interfaces: A Review of Electroencephalography Signals, How They Are Recorded, and Why They Matter.

Authors:  Kevin M Pitt; Jonathan S Brumberg; Jeremy D Burnison; Jyutika Mehta; Juhi Kidwai
Journal:  Perspect ASHA Spec Interest Groups       Date:  2019-11-09

6.  Evaluating person-centered factors associated with brain-computer interface access to a commercial augmentative and alternative communication paradigm.

Authors:  Kevin M Pitt; Jonathan S Brumberg
Journal:  Assist Technol       Date:  2021-03-05

7.  Expansion of C9ORF72 in amyotrophic lateral sclerosis correlates with brain-computer interface performance.

Authors:  Andrew Geronimo; Kathryn E Sheldon; James R Broach; Zachary Simmons; Steven J Schiff
Journal:  Sci Rep       Date:  2017-08-21       Impact factor: 4.379

Review 8.  EEG-Based Brain-Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21 st Century.

Authors:  Ioulietta Lazarou; Spiros Nikolopoulos; Panagiotis C Petrantonakis; Ioannis Kompatsiaris; Magda Tsolaki
Journal:  Front Hum Neurosci       Date:  2018-01-31       Impact factor: 3.169

9.  Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher's Criterion-Based Channel Selection.

Authors:  Yi-Hung Liu; Shiuan Huang; Yi-De Huang
Journal:  Sensors (Basel)       Date:  2017-07-03       Impact factor: 3.576

Review 10.  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

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.