Literature DB >> 27841159

Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS.

Tatiana Fomina1, Gabriele Lohmann, Michael Erb, Thomas Ethofer, Bernhard Schölkopf, Moritz Grosse-Wentrup.   

Abstract

OBJECTIVE: Electroencephalographic (EEG) brain-computer interfaces (BCIs) hold promise in restoring communication for patients with completely locked-in stage amyotrophic lateral sclerosis (ALS). However, these patients cannot use existing EEG-based BCIs, arguably because such systems rely on brain processes that are impaired in the late stages of ALS. In this work, we introduce a novel BCI designed for patients in late stages of ALS based on high-level cognitive processes that are less likely to be affected by ALS. APPROACH: We trained two ALS patients via EEG-based neurofeedback to use self-regulation of theta or gamma oscillations in the precuneus for basic communication. Because there is a tight connection between the precuneus and consciousness, precuneus oscillations are arguably generated by high-level cognitive processes, which are less likely to be affected by ALS than processes linked to the peripheral nervous system. MAIN
RESULTS: Both patients learned to self-regulate their precuneus oscillations and achieved stable online decoding accuracy over the course of disease progression. One patient achieved a mean online decoding accuracy in a binary decision task of 70.55% across 26 training sessions, and the other patient achieved 59.44% across 16 training sessions. We provide empirical evidence that these oscillations were cortical in nature and originated from the intersection of the precuneus, cuneus, and posterior cingulate. SIGNIFICANCE: Our results establish that ALS patients can employ self-regulation of precuneus oscillations for communication. Such a BCI is likely to be available to ALS patients as long as their consciousness supports communication.

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Year:  2016        PMID: 27841159     DOI: 10.1088/1741-2560/13/6/066021

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


  7 in total

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

Authors:  Seyyed Bahram Borgheai; John McLinden; Alyssa Hillary Zisk; Sarah Ismail Hosni; Roohollah Jafari Deligani; Mohammadreza Abtahi; Kunal Mankodiya; Yalda Shahriari
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2020-03-13       Impact factor: 3.802

2.  Eyes-closed hybrid brain-computer interface employing frontal brain activation.

Authors:  Jaeyoung Shin; Klaus-Robert Müller; Han-Jeong Hwang
Journal:  PLoS One       Date:  2018-05-07       Impact factor: 3.240

3.  Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster.

Authors:  Xia-An Bi; Jie Chen; Qi Sun; Yingchao Liu; Yang Wang; Xianhao Luo
Journal:  Front Physiol       Date:  2018-11-21       Impact factor: 4.566

4.  A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum Disorder.

Authors:  Marco Simões; Raquel Monteiro; João Andrade; Susana Mouga; Felipe França; Guiomar Oliveira; Paulo Carvalho; Miguel Castelo-Branco
Journal:  Front Neurosci       Date:  2018-11-01       Impact factor: 4.677

5.  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

6.  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

7.  A conceptual space for EEG-based brain-computer interfaces.

Authors:  Nataliya Kosmyna; Anatole Lécuyer
Journal:  PLoS One       Date:  2019-01-03       Impact factor: 3.240

  7 in total

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