Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation

Ju Chi Liu, Hung Chyun Chou, Chien Hsiu Chen, Yi Tseng Lin, Chung Hsien Kuo

研究成果: 雜誌貢獻文章同行評審

4 引文 斯高帕斯(Scopus)

摘要

A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI). The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM) and accuracy-recognition mode (AM), were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR). When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints.

原文英語
文章編號3039454
期刊Computational Intelligence and Neuroscience
2016
DOIs
出版狀態已發佈 - 2016

ASJC Scopus subject areas

  • 電腦科學(全部)
  • 數學(全部)
  • 神經科學 (全部)

指紋

深入研究「Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation」主題。共同形成了獨特的指紋。

引用此