Cortically coupled computer vision with Emotiv headset using distractor variables

被引:0
|
作者
Ousterhout, Thomas [1 ]
Dyrholm, Mads [2 ]
机构
[1] Univ Copenhagen, Ctr Language Technol, Njalsgade 140, DK-2300 Copenhagen S, Denmark
[2] Univ Copenhagen, Dept Psychol, DK-1353 Copenhagen S, Denmark
关键词
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Visual search tasks can take long amounts of time and the more complicated the image is the longer it takes to find a target. Therefore, it is of interest to come up with a system that can augment a searcher's vision, in relation to speed, enabling the searcher to find the target faster than through normal means. Audition and vision are important for both communicative and informational purposes and therefore neurological signatures related to such abilities can greater increase cognitive infocommunicative devices. The P300 neurological response is a well known event-related potential that identifies the recognition of a target in such a search task. Clinical electroencephalogram (EEG) technology has previously been used to detect the P300 signature in response to target recognition during a Rapid Serial Visual Presentation (RSVP). Following the oddball paradigm, this study uses the 14-electrode Emotiv EPOC to detect the P300 in eight subjects performing a novel Where's Waldo target recognition task with the success criterion to optimize their completion time. In this paradigm, the number of targets and distractors are varied following a geometric distribution in order to have the participants engage in the task throughout the session. Although the Emotiv EPOC is crude in comparison to other EEG systems such as those with 256 electrodes, our results show better search times compared to baseline, and thus proves the feasibility of augmented vision even with such a limited system.
引用
收藏
页码:245 / 249
页数:5
相关论文
共 50 条
  • [21] Using Computer Vision to See
    Mocanu, Bogdan
    Tapu, Ruxandra
    Zaharia, Titus
    COMPUTER VISION - ECCV 2016 WORKSHOPS, PT II, 2016, 9914 : 375 - 390
  • [22] Development of headset-type computer mouse using gyro sensors for the handicapped
    Kim, YW
    ELECTRONICS LETTERS, 2002, 38 (22) : 1313 - 1314
  • [23] Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia
    Ng, Kimberley-Dale
    Mehdizadeh, Sina
    Iaboni, Andrea
    Mansfield, Avril
    Flint, Alastair
    Taati, Babak
    IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE, 2020, 8
  • [24] Cytotoxicity variables in high-throughput computer-vision studies
    Heroux, P.
    Li, Y.
    TOXICOLOGY LETTERS, 2010, 196 : S153 - S154
  • [25] Estimation of nonlinear errors-in-variables models for computer vision applications
    Matei, Bogdan C.
    Meer, Peter
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2006, 28 (10) : 1537 - 1552
  • [26] OYSTER ORIENTATION USING COMPUTER VISION
    TOJEIRO, P
    WHEATON, F
    TRANSACTIONS OF THE ASAE, 1991, 34 (02): : 689 - 693
  • [27] Virtual worlds using computer vision
    Narayanan, PJ
    Kanade, T
    COMPUTER VISION FOR VIRTUAL REALITY BASED HUMAN COMMUNICATIONS - 1998 IEEE AND ATR WORKSHOP PROCEEDINGS, 1998, : 2 - 13
  • [28] GUI Testing Using Computer Vision
    Chang, Tsung-Hsiang
    Yeh, Tom
    Miller, Robert C.
    CHI2010: PROCEEDINGS OF THE 28TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, 2010, : 1535 - +
  • [29] Flood Monitoring using Computer Vision
    Nair, Bhavana B.
    Rao, Sethuraman N.
    MOBISYS'17: PROCEEDINGS OF THE 15TH ANNUAL INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS, APPLICATIONS, AND SERVICES, 2017, : 165 - 165
  • [30] TEXTURE ANALYSIS USING COMPUTER VISION
    DAMODARASAMY, S
    RAMAN, S
    COMPUTERS IN INDUSTRY, 1991, 16 (01) : 25 - 34