A P300-Based Brain-Computer Interface for Improving Attention

被引:39
|
作者
Arvaneh, Mahnaz [1 ]
Robertson, Ian H. [2 ]
Ward, Tomas E. [3 ]
机构
[1] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield, S Yorkshire, England
[2] Trinity Coll Dublin, Global Brain Hlth Inst, Inst Neurosci, Dublin, Ireland
[3] Dublin City Univ, Insight Ctr Data Analyt, Sch Comp, Dublin, Ireland
来源
基金
爱尔兰科学基金会;
关键词
brain-computer interface; neurofeedback; P300; attention; electroencephalography; EEG-NEUROFEEDBACK; P300; BCI; COMMUNICATION; PERFORMANCE; SIGNALS;
D O I
10.3389/fnhum.2018.00524
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
A Brain-computer Interface (BCI) can be used as a neurofeedback training tool to improve cognitive performance. BCIs aim to improve the effectiveness and efficiency of the conventional neurofeedback methods by focusing on the self-regulation of individualized neuromarkers rather than generic ones in a graphically appealing training environment. In this work, for the first time, we have modified a widely used P300-based speller BCI and used it as an engaging neurofeedack training game to enhance P300. According to the user's performance the game becomes more difficult in an adaptive manner, requiring the generation of a larger and stronger P300 (i.e., in terms of total energy) in response to target stimuli. Since the P300 is generated naturally without conscious effort in response to a target trial, unlike many rhythm-based neurofeedback tools, the ability to control the proposed P300-based neurofeedback training is obtained after a short calibration without undergoing tedious trial and error sessions. The performance of the proposed neurofeedback training was evaluated over a short time scale (approximately 30 min training) using 28 young adult participants who were randomly assigned to either the experimental group or the control group. In summary, our results show that the proposed P300-based BCI neurofeedback training yielded a significant enhancement in the ERP components of the target trials (i.e., 150-550 ms after the onset of stimuli which includes P300) as well as attenuation in the corresponding ERP components of the non-target trials. In addition, more centro-parietal alpha suppression was observed in the experimental group during the neurofeedback training as well as a post-training spatial attention task. Interestingly, a significant improvement in the response time of a spatial attention task performed immediately after the neurofeedback training was observed in the experimental group. This paper, as a proof-of-concept study, suggests that the proposed neurofeedback training tool is a promising tool for improving attention particularly for those who are at risk of attention deficiency.
引用
收藏
页数:14
相关论文
共 50 条
  • [31] The P300-based brain-computer interface (BCI): Effects of stimulus rate
    McFarland, Dennis J.
    Sarnacki, William A.
    Townsend, George
    Vaughan, Theresa
    Wolpaw, Jonathan R.
    [J]. CLINICAL NEUROPHYSIOLOGY, 2011, 122 (04) : 731 - 737
  • [32] Channel Selection Based on Phase Measurement in P300-Based Brain-Computer Interface
    Xu, Minpeng
    Qi, Hongzhi
    Ma, Lan
    Sun, Changcheng
    Zhang, Lixin
    Wan, Baikun
    Yin, Tao
    Ming, Dong
    [J]. PLOS ONE, 2013, 8 (04):
  • [33] Dependence N200 and P300 ERPs in P300-based Brain-Computer Interface on the Variations of Voluntary Attention
    Basyul, I. A.
    Kaplan, A. Ya.
    [J]. ZHURNAL VYSSHEI NERVNOI DEYATELNOSTI IMENI I P PAVLOVA, 2014, 64 (02) : 159 - 165
  • [34] Classification effects of real and imaginary movement selective attention tasks on a P300-based brain-computer interface
    Salvaris, Mathew
    Sepulveda, Francisco
    [J]. JOURNAL OF NEURAL ENGINEERING, 2010, 7 (05)
  • [35] Toward a high-throughput auditory P300-based brain-computer interface
    Klobassa, D. S.
    Vaughan, T. M.
    Brunner, P.
    Schwartz, N. E.
    Wolpaw, J. R.
    Neuper, C.
    Sellers, E. W.
    [J]. CLINICAL NEUROPHYSIOLOGY, 2009, 120 (07) : 1252 - 1261
  • [36] P300-based Brain-Computer Interface Memory Game to Improve Motivation and Performance
    Angeloni, C.
    Salter, D.
    Corbit, V.
    Lorence, T.
    Yu, Y-C.
    Gabel, L. A.
    [J]. 2012 38TH ANNUAL NORTHEAST BIOENGINEERING CONFERENCE (NEBEC), 2012, : 35 - +
  • [37] Effects of Spatial Stimulus Overlap in a Visual P300-based Brain-computer Interface
    Fernandez-Rodriguez, Alvaro
    Teresa Medina-Julia, Maria
    Velasco-Alvarez, Francisco
    Ron-Angevin, Ricardo
    [J]. NEUROSCIENCE, 2020, 431 : 134 - 142
  • [38] Design and Implementation of a P300-Based Brain-Computer Interface for Controlling an Internet Browser
    Mugler, Emily M.
    Ruf, Carolin A.
    Halder, Sebastian
    Bensch, Michael
    Kuebler, Andrea
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2010, 18 (06) : 599 - 609
  • [39] Evaluation of P300-Based Brain-Computer Interface in Real-World Contexts
    Nam, Chang S.
    Li, Yueqing
    Johnson, Steve
    [J]. INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, 2010, 26 (06) : 621 - 637
  • [40] An Auditory P300-based Brain-Computer Interface Using Ear-EEG
    Kaongoen, Netiwit
    Jo, Sungho
    [J]. 2018 6TH INTERNATIONAL CONFERENCE ON BRAIN-COMPUTER INTERFACE (BCI), 2018, : 134 - 137