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Computational Intelligence and Neuroscience
Volume 2016, Article ID 3039454, 22 pages
http://dx.doi.org/10.1155/2016/3039454
Research Article

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

1Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
2Division of Cardiology, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City 235, Taiwan
3Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan
4Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan
5Department of Biomedical Engineering, National Defense Medical Center, Taipei 114, Taiwan

Received 2 March 2016; Revised 23 May 2016; Accepted 22 June 2016

Academic Editor: Paolo Del Giudice

Copyright © 2016 Ju-Chi Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.