ON A GAMIFIED BRAIN-COMPUTER INTERFACE FOR COGNITIVE TRAINING OF SPATIAL WORKING MEMORY

被引:0
|
作者
Liu, Ziming [1 ]
Bryan, Jonathan [1 ]
Borkoski, Robert [1 ]
Yuan, Fengpei [1 ]
Li, Yansong [2 ]
Zhao, Xiaopeng [1 ]
机构
[1] Univ Tennessee, Dept Mech Aerosp & Biomed Engn, Knoxville, TN 37996 USA
[2] Univ Calif San Diego, Jacobs Sch Engn, San Diego, CA 92093 USA
来源
PROCEEDINGS OF THE ASME DYNAMIC SYSTEMS AND CONTROL CONFERENCE, DSCC2020, VOL 1 | 2020年
关键词
cognitive training; gamification; brain-computer interface; spatial working memory; EEG BIOFEEDBACK; ATTENTION-DEFICIT; CHILDREN; SYMPTOMS; DEMENTIA; DISORDER;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the United States, there are a large number of people suffering from memory and attention deficit problems. For example, patients with attention-deficit hyperactivity disorder (ADHD) and dementia have difficulties in performing activities of daily living and have a low quality of life. Currently, there exist no effective treatment for these memory and attention issues in specific cognitive impairments. In this paper, we developed a gamified platform of brain-computer interface (BCI) for cognitive training, which can engage users in the training and provide users qualitative and quantitative feedback for their training of spatial working memory. The user is able to control the movement of a drone using motor imager, which is imagined movement of body part. Sensorimotor rhythms of the user are calculated using the user's EEG to drive the movement of the drone. Twenty normal healthy subjects were recruited to test the user experience. Our system showed the capability of engaging users, good robustness, user acceptability and usability. Therefore, we think our platform might be an alternative to provide more accessible, engaging, and effective cognitive training for people with memory and attention problems. In future, we will test the usability and effectiveness of the system for cognitive training in patients with ADHD and dementia.
引用
收藏
页数:6
相关论文
共 50 条
  • [41] Sparse spatial filter optimization for EEG channel reduction in brain-computer interface
    Yong, Xinyi
    Ward, Rabab K.
    Birch, Gary E.
    2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12, 2008, : 417 - 420
  • [42] Simulation of sports movement training based on machine learning and brain-computer interface
    Wang, Linuo
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 40 (04) : 6409 - 6420
  • [43] Cognitive ability assessment by Brain-Computer Interface Validation of a new assessment method for cognitive abilities
    Perego, P.
    Turconi, A. C.
    Andreoni, G.
    Maggi, L.
    Beretta, E.
    Parini, S.
    Gagliardi, C.
    JOURNAL OF NEUROSCIENCE METHODS, 2011, 201 (01) : 239 - 250
  • [44] Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system
    Sieghartsleitner, Sebastian
    Sebastian-Romagosa, Marc
    Cho, Woosang
    Grunwald, Johannes
    Ortner, Rupert
    Scharinger, Josef
    Kamada, Kyousuke
    Guger, Christoph
    FRONTIERS IN NEUROSCIENCE, 2024, 18
  • [45] Improving brain-computer interface classification using adaptive common spatial patterns
    Song, Xiaomu
    Yoon, Suk-Chung
    COMPUTERS IN BIOLOGY AND MEDICINE, 2015, 61 : 150 - 160
  • [46] Adaptive training session for a P300 speller brain-computer interface
    Rivet, Bertrand
    Cecotti, Hubert
    Perrin, Margaux
    Maby, Emmanuel
    Mattout, Jeremie
    JOURNAL OF PHYSIOLOGY-PARIS, 2011, 105 (1-3) : 123 - 129
  • [47] Bayesian Learning for Spatial Filtering in an EEG-Based Brain-Computer Interface
    Zhang, Haihong
    Yang, Huijuan
    Guan, Cuntai
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2013, 24 (07) : 1049 - 1060
  • [48] The changes in the hemodynamic activity of the brain during motor imagery training with the use of brain-computer interface
    Frolov A.A.
    Husek D.
    Silchenko A.V.
    Tintera J.
    Rydlo J.
    Human Physiology, 2016, 42 (1) : 1 - 12
  • [49] Effectiveness of a Brain-Computer Interface Based Programme for the Treatment of ADHD: A Pilot Study
    Lim, Choon Guan
    Lee, Tih-Shih
    Guan, Cuntai
    Fung, Daniel Shuen Sheng
    Cheung, Yin Bun
    Teng, Stephanie Sze Wei
    Zhang, Haihong
    Krishnan, K. Ranga
    PSYCHOPHARMACOLOGY BULLETIN, 2010, 43 (01) : 73 - 82
  • [50] Development of Brain-Computer Interface (BCI) System for Bridging Brain and Computer
    Kanoh, S.
    Miyamoto, K.
    Yoshinobu, T.
    13TH INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING, VOLS 1-3, 2009, 23 (1-3): : 2264 - 2267