title: Steady-State Motion Visual Evoked Potential (SSMVEP) Based on Equal Luminance Colored Enhancement.
abstract: 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.
authors.affiliation: School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
authors.forename: Wenqiang
authors.identifier: http://orcid.org/0000-0002-9878-3730
authors.initials: W
authors.lastname: Yan
authors.affiliation: School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.|State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China.
authors.forename: Guanghua
authors.initials: G
authors.lastname: Xu
authors.affiliation: School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
authors.forename: Min
authors.initials: M
authors.lastname: Li
authors.affiliation: School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Xie
authors.affiliation: School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
authors.forename: Chengcheng
authors.initials: C
authors.lastname: Han
authors.affiliation: School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.
authors.forename: Sicong
authors.initials: S
authors.lastname: Zhang
authors.affiliation: School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.|State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China.
authors.forename: Ailing
authors.initials: A
authors.lastname: Luo
authors.affiliation: Department of Biomedical Engineering, Wayne State University, Detroit, Michigan, United States of America.
authors.forename: Chaoyang
authors.initials: C
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D003118:Color Perception; D004569:Electroencephalography; D005074:Evoked Potentials, Visual; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009038:Motion; D009434:Neural Pathways; D010364:Pattern Recognition, Visual; D010775:Photic Stimulation; D015203:Reproducibility of Results; D014793:Visual Cortex / Q000502:physiology