Covert attention allows for continuous control of brain-computer interfaces

被引:49
|
作者
Bahramisharif, Ali [1 ,2 ]
van Gerven, Marcel [1 ,2 ]
Heskes, Tom [1 ,2 ]
Jensen, Ole [2 ]
机构
[1] Radboud Univ Nijmegen, Inst Comp & Informat Sci, NL-6525 ED Nijmegen, Netherlands
[2] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, NL-6525 ED Nijmegen, Netherlands
关键词
BCI; circular regression; magnetoencephalography; peripheral sensory stimulus; posterior alpha activity; VISUAL-SPATIAL ATTENTION; COMMUNICATION; POWER; OSCILLATIONS; SUPPRESSION; INHIBITION; INCREASES; DEVICES; BCI; MEG;
D O I
10.1111/j.1460-9568.2010.07174.x
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
While brain-computer interfaces (BCIs) can be used for controlling external devices, they also hold the promise of providing a new tool for studying the working brain. In this study we investigated whether modulations of brain activity by changes in covert attention can be used as a continuous control signal for BCI. Covert attention is the act of mentally focusing on a peripheral sensory stimulus without changing gaze direction. The ongoing brain activity was recorded using magnetoencephalography in subjects as they covertly attended to a moving cue while maintaining fixation. Based on posterior alpha power alone, the direction to which subjects were attending could be recovered using circular regression. Results show that the angle of attention could be predicted with a mean absolute deviation of 51 degrees in our best subject. Averaged over subjects, the mean deviation was similar to 70 degrees. In terms of information transfer rate, the optimal data length used for recovering the direction of attention was found to be 1700 ms; this resulted in a mean absolute deviation of 60 degrees for the best subject. The results were obtained without any subject-specific feature selection and did not require prior subject training. Our findings demonstrate that modulations of posterior alpha activity due to the direction of covert attention has potential as a control signal for continuous control in a BCI setting. Our approach will have several applications, including a brain-controlled computer mouse and improved methods for neuro-feedback that allow direct training of subjects' ability to modulate posterior alpha activity.
引用
收藏
页码:1501 / 1508
页数:8
相关论文
共 50 条
  • [21] Multimodal Brain-Computer Interfaces
    Alexander Maye
    Andreas K.Engel
    Tsinghua Science and Technology, 2011, 16 (02) : 133 - 139
  • [22] An update for brain-computer interfaces
    不详
    NATURE ELECTRONICS, 2024, 7 (09): : 725 - 725
  • [23] Brain-computer interfaces (BCIs)
    Berger, Theodore W.
    JOURNAL OF NEUROSCIENCE METHODS, 2008, 167 (01) : 1 - 1
  • [24] Optogenetic Brain-Computer Interfaces
    Tang, Feifang
    Yan, Feiyang
    Zhong, Yushan
    Li, Jinqian
    Gong, Hui
    Li, Xiangning
    BIOENGINEERING-BASEL, 2024, 11 (08):
  • [25] The business of brain-computer interfaces
    Smalley, Eric
    NATURE BIOTECHNOLOGY, 2019, 37 (09) : 978 - 982
  • [26] The Future of Brain-Computer Interfaces
    Abrams, Zara
    IEEE PULSE, 2022, 13 (06) : 21 - 24
  • [27] The physiology of brain-computer interfaces
    Cohen, Leonardo G.
    Birbaumer, Niels
    JOURNAL OF PHYSIOLOGY-LONDON, 2007, 579 (03): : 570 - 570
  • [28] fMRI brain-computer interfaces
    Sitaram, Ranganatha
    Weiskopf, Nikolaus
    Caria, Andrea
    Veit, Ralf
    Erb, Michael
    Birbaumer, Niels
    IEEE SIGNAL PROCESSING MAGAZINE, 2008, 25 (01) : 95 - 106
  • [29] Improving Brain-Computer Interfaces
    Kroeker, Kirk L.
    COMMUNICATIONS OF THE ACM, 2011, 54 (10) : 11 - 14
  • [30] The year of brain-computer interfaces
    不详
    NATURE ELECTRONICS, 2023, 6 (09) : 643 - 643