Literature DB >> 23204287

Using a hybrid brain computer interface and virtual reality system to monitor and promote cortical reorganization through motor activity and motor imagery training.

S Bermúdez i Badia1, A García Morgade, H Samaha, P F M J Verschure.   

Abstract

Stroke is one of the leading causes of adult disability with high economical and societal costs. In recent years, novel rehabilitation paradigms have been proposed to address the life-long plasticity of the brain to regain motor function. We propose a hybrid brain-computer interface (BCI)-virtual reality (VR) system that combines a personalized motor training in a VR environment, exploiting brain mechanisms for action execution and observation, and a neuro-feedback paradigm using mental imagery as a way to engage secondary or indirect pathways to access undamaged cortico-spinal tracts. Furthermore, we present the development and validation experiments of the proposed system. More specifically, EEG data on nine naïve healthy subjects show that a simultaneous motor activity and motor imagery paradigm is more effective at engaging cortical motor areas and related networks to a larger extent. Additionally, we propose a motor imagery driven BCI-VR version of our system that was evaluated with nine different healthy subjects. Data show that users are capable of controlling a virtual avatar in a motor imagery training task that dynamically adjusts its difficulty to the capabilities of the user. User self-report questionnaires indicate enjoyment and acceptance of the proposed system.

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Year:  2012        PMID: 23204287     DOI: 10.1109/TNSRE.2012.2229295

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


  9 in total

1.  Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose.

Authors:  Saugat Bhattacharyya; Amit Konar; D N Tibarewala
Journal:  Med Biol Eng Comput       Date:  2014-09-30       Impact factor: 2.602

2.  Applications of Brain-Machine Interface Systems in Stroke Recovery and Rehabilitation.

Authors:  Anusha Venkatakrishnan; Gerard E Francisco; Jose L Contreras-Vidal
Journal:  Curr Phys Med Rehabil Rep       Date:  2014-06-01

3.  Z-score linear discriminant analysis for EEG based brain-computer interfaces.

Authors:  Rui Zhang; Peng Xu; Lanjin Guo; Yangsong Zhang; Peiyang Li; Dezhong Yao
Journal:  PLoS One       Date:  2013-09-13       Impact factor: 3.240

4.  The Study of Evaluation and Rehabilitation of Patients With Different Cognitive Impairment Phases Based on Virtual Reality and EEG.

Authors:  Dong Wen; Xifa Lan; Yanhong Zhou; Guolin Li; Sheng-Hsiou Hsu; Tzyy-Ping Jung
Journal:  Front Aging Neurosci       Date:  2018-04-03       Impact factor: 5.750

Review 5.  A prescription for "nature" - the potential of using virtual nature in therapeutics.

Authors:  Matthew P White; Nicola L Yeo; Peeter Vassiljev; Rikard Lundstedt; Mattias Wallergård; Maria Albin; Mare Lõhmus
Journal:  Neuropsychiatr Dis Treat       Date:  2018-11-08       Impact factor: 2.570

Review 6.  Decoding Movement From Electrocorticographic Activity: A Review.

Authors:  Ksenia Volkova; Mikhail A Lebedev; Alexander Kaplan; Alexei Ossadtchi
Journal:  Front Neuroinform       Date:  2019-12-03       Impact factor: 4.081

7.  Penalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording.

Authors:  Andrey Eliseyev; Tetiana Aksenova
Journal:  PLoS One       Date:  2016-05-19       Impact factor: 3.240

8.  Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis.

Authors:  Athanasios Vourvopoulos; Sergi Bermúdez I Badia
Journal:  J Neuroeng Rehabil       Date:  2016-08-09       Impact factor: 4.262

9.  Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method.

Authors:  Patricia Batres-Mendoza; Mario A Ibarra-Manzano; Erick I Guerra-Hernandez; Dora L Almanza-Ojeda; Carlos R Montoro-Sanjose; Rene J Romero-Troncoso; Horacio Rostro-Gonzalez
Journal:  Comput Intell Neurosci       Date:  2017-12-03
  9 in total

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