Literature DB >> 26621216

Increasing N200 Potentials Via Visual Stimulus Depicting Humanoid Robot Behavior.

Mengfan Li1, Wei Li1,2, Huihui Zhou3,4.   

Abstract

Achieving recognizable visual event-related potentials plays an important role in improving the success rate in telepresence control of a humanoid robot via N200 or P300 potentials. The aim of this research is to intensively investigate ways to induce N200 potentials with obvious features by flashing robot images (images with meaningful information) and by flashing pictures containing only solid color squares (pictures with incomprehensible information). Comparative studies have shown that robot images evoke N200 potentials with recognizable negative peaks at approximately 260 ms in the frontal and central areas. The negative peak amplitudes increase, on average, from 1.2 μV, induced by flashing the squares, to 6.7 μV, induced by flashing the robot images. The data analyses support that the N200 potentials induced by the robot image stimuli exhibit recognizable features. Compared with the square stimuli, the robot image stimuli increase the average accuracy rate by 9.92%, from 83.33% to 93.25%, and the average information transfer rate by 24.56 bits/min, from 72.18 bits/min to 96.74 bits/min, in a single repetition. This finding implies that the robot images might provide the subjects with more information to understand the visual stimuli meanings and help them more effectively concentrate on their mental activities.

Keywords:  Content; N200; P300; mind-controlled robot; visual stimulus

Mesh:

Year:  2015        PMID: 26621216     DOI: 10.1142/S0129065715500392

Source DB:  PubMed          Journal:  Int J Neural Syst        ISSN: 0129-0657            Impact factor:   5.866


  3 in total

1.  Object Extraction in Cluttered Environments via a P300-Based IFCE.

Authors:  Xiaoqian Mao; Wei Li; Huidong He; Bin Xian; Ming Zeng; Huihui Zhou; Linwei Niu; Genshe Chen
Journal:  Comput Intell Neurosci       Date:  2017-06-27

Review 2.  Progress in EEG-Based Brain Robot Interaction Systems.

Authors:  Xiaoqian Mao; Mengfan Li; Wei Li; Linwei Niu; Bin Xian; Ming Zeng; Genshe Chen
Journal:  Comput Intell Neurosci       Date:  2017-04-05

3.  Separable EEG Features Induced by Timing Prediction for Active Brain-Computer Interfaces.

Authors:  Jiayuan Meng; Minpeng Xu; Kun Wang; Qiangfan Meng; Jin Han; Xiaolin Xiao; Shuang Liu; Dong Ming
Journal:  Sensors (Basel)       Date:  2020-06-25       Impact factor: 3.576

  3 in total

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