Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation
Journal
Computational Intelligence and Neuroscience
Journal Volume
2016
Date Issued
2016
Author(s)
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. ? 2016 Ju-Chi Liu et al.
Subjects
Anthropomorphic robots
Bioelectric potentials
Frequency modulation
Interfaces (computer)
Neural networks
Stress intensity factors
Correlation algorithm
Event related potentials
Fast recognition
High efficient
Information transfer rate
Operating modes
Time uncertainty
Visual stimulus
Brain computer interface
algorithm
biological model
brain
brain computer interface
computer simulation
electroencephalography
event related potential
feedback system
female
human
male
photostimulation
physiology
signal processing
time perception
young adult
Algorithms
Brain
Brain-Computer Interfaces
Computer Simulation
Electroencephalography
Event-Related Potentials, P300
Feedback
Female
Humans
Male
Models, Neurological
Photic Stimulation
Signal Processing, Computer-Assisted
Time Perception
Young Adult
Type
journal article