Gao Depeng, Wang Yujuan, Fu Peirong, Qiu Jianlin, Li Hongqi
School of Yonyou Digital and Intelligence, Nantong Institute of Technology, Nantong 226000, China.
School of Software, Northwestern Polytechnical University, Xi'an 710000, China.
Sensors (Basel). 2025 Mar 10;25(6):1706. doi: 10.3390/s25061706.
While steady-state visual evoked potentials (SSVEPs) are widely used in brain-computer interfaces (BCIs) due to their robustness to rhythmic visual stimuli, their generation mechanisms remain poorly understood. Challenges such as experimental complexity, inter-subject variability, and limited physiological interpretability hinder the development of efficient BCI systems. This study employed a single-channel neural mass model (NMM) of V1 cortical dynamics to investigate the biophysical underpinnings of SSVEP generation. By systematically varying synaptic gain, time constants, and external input parameters, we simulated band oscillations and analyzed their generation principles. The model demonstrates that synaptic gain controls oscillation amplitude and harmonic content, and time constants determine signal decay kinetics and frequency precision, while input variance modulates harmonic stability. Our results reveal how V1 circuitry generates frequency-locked SSVEP responses through excitatory-inhibitory interactions and dynamic filtering mechanisms. This computational framework successfully reproduces fundamental SSVEP characteristics without requiring multi-subject experimental data, offering new insights into the physiological basis of SSVEP-based brain-computer interfaces.
虽然稳态视觉诱发电位（SSVEPs）因其对节律性视觉刺激具有鲁棒性而在脑机接口（BCIs）中被广泛应用，但其产生机制仍知之甚少。诸如实验复杂性、个体间变异性以及生理可解释性有限等挑战阻碍了高效脑机接口系统的发展。本研究采用V1皮质动力学的单通道神经团模型（NMM）来研究SSVEP产生的生物物理基础。通过系统地改变突触增益、时间常数和外部输入参数，我们模拟了频段振荡并分析了它们的产生原理。该模型表明，突触增益控制振荡幅度和谐波含量，时间常数决定信号衰减动力学和频率精度，而输入方差调节谐波稳定性。我们的结果揭示了V1神经回路如何通过兴奋性-抑制性相互作用和动态滤波机制产生频率锁定的SSVEP反应。这个计算框架成功地再现了基本的SSVEP特征，而无需多主体实验数据，为基于SSVEP的脑机接口的生理基础提供了新的见解。
