Literature DB >> 33777542

A Usability Study of Low-cost Wireless Brain-Computer Interface for Cursor Control Using Online Linear Model.

Reza Abiri1, Soheil Borhani2, Justin Kilmarx2, Connor Esterwood3, Yang Jiang4, Xiaopeng Zhao5.   

Abstract

Computer cursor control using electroencephalogram (EEG) signals is a common and well-studied brain-computer interface (BCI). The emphasis of the literature has been primarily on evaluation of the objective measures of assistive BCIs such as accuracy of the neural decoder whereas the subjective measures such as user's satisfaction play an essential role for the overall success of a BCI. As far as we know, the BCI literature lacks a comprehensive evaluation of the usability of the mind-controlled computer cursor in terms of decoder efficiency (accuracy), user experience, and relevant confounding variables concerning the platform for the public use. To fill this gap, we conducted a two-dimensional EEG-based cursor control experiment among 28 healthy participants. The computer cursor velocity was controlled by the imagery of hand movement using a paradigm presented in the literature named imagined body kinematics (IBK) with a low-cost wireless EEG headset. We evaluated the usability of the platform for different objective and subjective measures while we investigated the extent to which the training phase may influence the ultimate BCI outcome. We conducted pre- and post- BCI experiment interview questionnaires to evaluate the usability. Analyzing the questionnaires and the testing phase outcome shows a positive correlation between the individuals' ability of visualization and their level of mental controllability of the cursor. Despite individual differences, analyzing training data shows the significance of electrooculogram (EOG) on the predictability of the linear model. The results of this work may provide useful insights towards designing a personalized user-centered assistive BCI.

Entities:  

Keywords:  Brain-Computer Interface; Confounding variables; Cursor control; EEG; Imagined Body Kinematics; Usability

Year:  2020        PMID: 33777542      PMCID: PMC7990128          DOI: 10.1109/thms.2020.2983848

Source DB:  PubMed          Journal:  IEEE Trans Hum Mach Syst        ISSN: 2168-2291            Impact factor:   2.968


  36 in total

1.  BCI2000: a general-purpose brain-computer interface (BCI) system.

Authors:  Gerwin Schalk; Dennis J McFarland; Thilo Hinterberger; Niels Birbaumer; Jonathan R Wolpaw
Journal:  IEEE Trans Biomed Eng       Date:  2004-06       Impact factor: 4.538

2.  Mobile EEG and its potential to promote the theory and application of imagery-based motor rehabilitation.

Authors:  Cornelia Kranczioch; Catharina Zich; Irina Schierholz; Annette Sterr
Journal:  Int J Psychophysiol       Date:  2013-10-18       Impact factor: 2.997

3.  Multichannel EEG-based brain-computer communication.

Authors:  J R Wolpaw; D J McFarland
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1994-06

4.  Decoding Three-Dimensional Trajectory of Executed and Imagined Arm Movements From Electroencephalogram Signals.

Authors:  Jeong-Hun Kim; Felix Bießmann; Seong-Whan Lee
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2014-12-02       Impact factor: 3.802

5.  Optimizing Prediction Model for a Noninvasive Brain-Computer Interface Platform Using Channel Selection, Classification, and Regression.

Authors:  Soheil Borhani; Justin Kilmarx; David Saffo; Lucien Ng; Reza Abiri; Xiaopeng Zhao
Journal:  IEEE J Biomed Health Inform       Date:  2019-01-11       Impact factor: 5.772

6.  Decoding of velocities and positions of 3D arm movement from EEG.

Authors:  Patrick Ofner; Gernot R Müller-Putz
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2012

7.  Electroencephalographic (EEG) control of three-dimensional movement.

Authors:  Dennis J McFarland; William A Sarnacki; Jonathan R Wolpaw
Journal:  J Neural Eng       Date:  2010-05-11       Impact factor: 5.379

8.  Validation of ICA as a tool to remove eye movement artifacts from EEG/ERP.

Authors:  Maarten Mennes; Heidi Wouters; Bart Vanrumste; Lieven Lagae; Peter Stiers
Journal:  Psychophysiology       Date:  2010-11       Impact factor: 4.016

9.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans.

Authors:  Jonathan R Wolpaw; Dennis J McFarland
Journal:  Proc Natl Acad Sci U S A       Date:  2004-12-07       Impact factor: 11.205

10.  Decoding hand movement velocity from electroencephalogram signals during a drawing task.

Authors:  Jun Lv; Yuanqing Li; Zhenghui Gu
Journal:  Biomed Eng Online       Date:  2010-10-28       Impact factor: 2.819

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  2 in total

Review 1.  Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work?

Authors:  Yang Jiang; William Jessee; Stevie Hoyng; Soheil Borhani; Ziming Liu; Xiaopeng Zhao; Lacey K Price; Walter High; Jeremiah Suhl; Sylvia Cerel-Suhl
Journal:  Front Aging Neurosci       Date:  2022-03-28       Impact factor: 5.702

2.  Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis.

Authors:  Juai Wu; Zhenyu Wang; Tianheng Xu; Chengyang Sun
Journal:  Sensors (Basel)       Date:  2022-07-28       Impact factor: 3.847

  2 in total

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