Literature DB >> 32175867

Enhancing Communication for People in Late-Stage ALS Using an fNIRS-Based BCI System.

Seyyed Bahram Borgheai, John McLinden, Alyssa Hillary Zisk, Sarah Ismail Hosni, Roohollah Jafari Deligani, Mohammadreza Abtahi, Kunal Mankodiya, Yalda Shahriari.   

Abstract

OBJECTIVE: Brain-computer interface (BCI) based communication remains a challenge for people with later-stage amyotrophic lateral sclerosis (ALS) who lose all voluntary muscle control. Although recent studies have demonstrated the feasibility of functional near-infrared spectroscopy (fNIRS) to successfully control BCIs primarily for healthy cohorts, these systems are yet inefficient for people with severe motor disabilities like ALS. In this study, we developed a new fNIRS-based BCI system in concert with a single-trial Visuo-Mental (VM) paradigm to investigate the feasibility of enhanced communication for ALS patients, particularly those in the later stages of the disease.
METHODS: In the first part of the study, we recorded data from six ALS patients using our proposed protocol (fNIRS-VM) and compared the results with the conventional electroencephalography (EEG)-based multi-trial P3Speller (P3S). In the second part, we recorded longitudinal data from one patient in the late locked-in state (LIS) who had fully lost eye-gaze control. Using statistical parametric mapping (SPM) and correlation analysis, the optimal channels and hemodynamic features were selected and used in linear discriminant analysis (LDA).
RESULTS: Over all the subjects, we obtained an average accuracy of 81.3%±5.7% within comparatively short times (< 4 sec) in the fNIRS-VM protocol relative to an average accuracy of 74.0%±8.9% in the P3S, though not competitive in patients with no substantial visual problems. Our longitudinal analysis showed substantially superior accuracy using the proposed fNIRS-VM protocol (73.2%±2.0%) over the P3S (61.8%±1.5%). SIGNIFICANCE: Our findings indicate the potential efficacy of our proposed system for communication and control for late-stage ALS patients.

Entities:  

Mesh:

Year:  2020        PMID: 32175867      PMCID: PMC7288752          DOI: 10.1109/TNSRE.2020.2980772

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  37 in total

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2.  NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy.

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Journal:  Neuroimage       Date:  2008-09-12       Impact factor: 6.556

3.  Application of a common spatial pattern-based algorithm for an fNIRS-based motor imagery brain-computer interface.

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Journal:  Neurosci Lett       Date:  2017-06-27       Impact factor: 3.046

4.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials.

Authors:  L A Farwell; E Donchin
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1988-12

Review 5.  Brain-computer interfaces in the completely locked-in state and chronic stroke.

Authors:  U Chaudhary; N Birbaumer; A Ramos-Murguialday
Journal:  Prog Brain Res       Date:  2016-08-08       Impact factor: 2.453

6.  Transition from the locked in to the completely locked-in state: a physiological analysis.

Authors:  A Ramos Murguialday; J Hill; M Bensch; S Martens; S Halder; F Nijboer; B Schoelkopf; N Birbaumer; A Gharabaghi
Journal:  Clin Neurophysiol       Date:  2010-12-09       Impact factor: 3.708

7.  Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface.

Authors:  A Kübler; F Nijboer; J Mellinger; T M Vaughan; H Pawelzik; G Schalk; D J McFarland; N Birbaumer; J R Wolpaw
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8.  Complete Locked-in and Locked-in Patients: Command Following Assessment and Communication with Vibro-Tactile P300 and Motor Imagery Brain-Computer Interface Tools.

Authors:  Christoph Guger; Rossella Spataro; Brendan Z Allison; Alexander Heilinger; Rupert Ortner; Woosang Cho; Vincenzo La Bella
Journal:  Front Neurosci       Date:  2017-05-05       Impact factor: 4.677

9.  Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS.

Authors:  Sarah D Power; Azadeh Kushki; Tom Chau
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10.  Comparison of eye tracking, electrooculography and an auditory brain-computer interface for binary communication: a case study with a participant in the locked-in state.

Authors:  Ivo Käthner; Andrea Kübler; Sebastian Halder
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  6 in total

1.  Multimodal fusion of EEG-fNIRS: a mutual information-based hybrid classification framework.

Authors:  Roohollah Jafari Deligani; Seyyed Bahram Borgheai; John McLinden; Yalda Shahriari
Journal:  Biomed Opt Express       Date:  2021-02-26       Impact factor: 3.732

2.  Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain-Machine Interface (BMI).

Authors:  Umer Asgher; Muhammad Jawad Khan; Muhammad Hamza Asif Nizami; Khurram Khalil; Riaz Ahmad; Yasar Ayaz; Noman Naseer
Journal:  Front Neurorobot       Date:  2021-03-18       Impact factor: 2.650

3.  See, Hear, or Feel - to Speak: A Versatile Multiple-Choice Functional Near-Infrared Spectroscopy-Brain-Computer Interface Feasible With Visual, Auditory, or Tactile Instructions.

Authors:  Laurien Nagels-Coune; Lars Riecke; Amaia Benitez-Andonegui; Simona Klinkhammer; Rainer Goebel; Peter De Weerd; Michael Lührs; Bettina Sorger
Journal:  Front Hum Neurosci       Date:  2021-11-25       Impact factor: 3.169

4.  Improved classification performance of EEG-fNIRS multimodal brain-computer interface based on multi-domain features and multi-level progressive learning.

Authors:  Lina Qiu; Yongshi Zhong; Zhipeng He; Jiahui Pan
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5.  Exploring the effects of head movements and accompanying gaze fixation switch on steady-state visual evoked potential.

Authors:  Junyi Duan; Songwei Li; Li Ling; Ning Zhang; Jianjun Meng
Journal:  Front Hum Neurosci       Date:  2022-09-12       Impact factor: 3.473

6.  Electrical and Hemodynamic Neural Functions in People With ALS: An EEG-fNIRS Resting-State Study.

Authors:  Roohollah Jafari Deligani; Sarah Ismail Hosni; Seyyed Bahram Borgheai; John McLinden; Alyssa Hillary Zisk; Kunal Mankodiya; Yalda Shahriari
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2021-01-28       Impact factor: 3.802

  6 in total

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