Steady-State Motion Visual Evoked Potential (SSMVEP) Based on Equal Luminance Colored Enhancement

• Published in: PloS one Vol. 12; no. 1; p. e0169642
• Main Authors: Yan, Wenqiang, Xu, Guanghua, Li, Min, Xie, Jun, Han, Chengcheng, Zhang, Sicong, Luo, Ailing, Chen, Chaoyang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.01.2017; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Biology and Life Sciences; Biomedical engineering; Brain; Brain research; Brain stimulation; Brain-Computer Interfaces; Color Perception; Computer applications; Contraction; Correlation analysis; EEG; Electroencephalography; Engineering and Technology; Evoked Potentials, Visual; Fatigue; Feature recognition; Female; Flicker; Frequency spectrum; Human performance; Human-computer interface; Humans; Implants; Laboratories; Low frequencies; Male; Mechanical engineering; Medicine and Health Sciences; Models, Neurological; Motion; Motion detection; Neural Pathways; Neurosciences; Pattern Recognition, Visual; Peak frequency; Photic Stimulation; Physical Sciences; Reproducibility of Results; Research and Analysis Methods; Statistical analysis; Steady state; Stimulation; Visual Cortex - physiology; Visual evoked potentials; Visual stimuli
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0169642

Steady-state visual evoked potential (SSVEP) is one of the typical stimulation paradigms of brain-computer interface (BCI). It has become a research approach to improve the performance of human-computer interaction, because of its advantages including multiple objectives, less recording electrodes for electroencephalogram (EEG) signals, and strong anti-interference capacity. Traditional SSVEP using light flicker stimulation may cause visual fatigue with a consequent reduction of recognition accuracy. To avoid the negative impacts on the brain response caused by prolonged strong visual stimulation for SSVEP, steady-state motion visual evoked potential (SSMVEP) stimulation method was used in this study by an equal-luminance colored ring-shaped checkerboard paradigm. The movement patterns of the checkerboard included contraction and expansion, which produced less discomfort to subjects. Feature recognition algorithms based on power spectrum density (PSD) peak was used to identify the peak frequency on PSD in response to visual stimuli. Results demonstrated that the equal-luminance red-green stimulating paradigm within the low frequency spectrum (lower than 15 Hz) produced higher power of SSMVEP and recognition accuracy than black-white stimulating paradigm. PSD-based SSMVEP recognition accuracy was 88.15±6.56%. There was no statistical difference between canonical correlation analysis (CCA) (86.57±5.37%) and PSD on recognition accuracy. This study demonstrated that equal-luminance colored ring-shaped checkerboard visual stimulation evoked SSMVEP with better SNR on low frequency spectrum of power density and improved the interactive performance of BCI.