Performance Evaluation of Visual Noise Imposed Stochastic Resonance Effect on Brain-Computer Interface Application: A Comparison Between Motion-Reversing Simple Ring and Complex Checkerboard Patterns

Frontiers in Neuroscience

卷:13

Guanglin Li¹  Peng Fang¹  Guangjing Du²  Tao Xue²  Min Li²  Guozhi Cao²  Yanjun Zhang²  Guanghua Xu³  Jun Xie³  Xingang Zhao⁴ 

[1] CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen, China;

[2] School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China;

[3] State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, China;

[4] State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China;

关键词: brain-computer interface (BCI);    visual noise;    stochastic resonance (SR);    motion-reversing stimulation;    checkerboard;    single ring;   

DOI  :  10.3389/fnins.2019.01192

来源: DOAJ

【 摘 要 】

Adding noise to a weak input signal can enhance the response of a non-linear system, a phenomenon known as stochastic resonance (SR). SR has been demonstrated in a variety of diverse sensory systems including the visual system, where visual noise enhances human motion perception and detection performance. The SR effect has not been extensively studied in brain-computer interface (BCI) applications. This study compares the performance of BCIs based on SR-influenced steady-state motion visual evoked potentials. Stimulation paradigms were used between a periodically monochromatic motion-reversing simple ring and complex alternating checkerboard stimuli. To induce the SR effect, dynamic visual noise was masked on both the periodic simple and complex stimuli. Offline results showed that the recognition accuracy of different stimulation targets followed an inverted U-shaped function of noise level, which is a hallmark of SR. With the optimal visual noise level, the proposed visual noise masked checkerboard BCI paradigm achieved faster and more stable detection performance due to the noise-enhanced brain responses. This work demonstrates that the SR effect can be employed in BCI applications and can achieve considerable performance improvements.

【 授权许可】

Unknown