# A Novel Multi-Dynamic Coupled Neural Mass Model of SSVEP

## TL;DR

This paper introduces a new neural model for simulating SSVEP signals, improving BCI efficiency and reliability without requiring physical EEG recordings.

## Contribution

A novel multi-dynamic coupled neural mass model for SSVEP simulation with dual-region coupling and frequency response integration.

## Key findings

- The model achieved high-precision SSVEP simulation with maximum errors decreasing from 2.2861 to 0.8430 across frequencies.
- Simulated SSVEP signals were successfully used for real-time control of an Arduino car via the FPF-net model.
- The model accommodates individual differences and neuronal density variations through parameter optimization.

## Abstract

Steady-state visual evoked potential (SSVEP)-based brain—computer interfaces (BCIs) leverage high-speed neural synchronization to visual flicker stimuli for efficient device control. While SSVEP-BCIs minimize user training requirements, their dependence on physical EEG recordings introduces challenges, such as inter-subject variability, signal instability, and experimental complexity. To overcome these limitations, this study proposes a novel neural mass model for SSVEP simulation by integrating frequency response characteristics with dual-region coupling mechanisms. Specific parallel linear transformation functions were designed based on SSVEP frequency responses, and weight coefficient matrices were determined according to the frequency band energy distribution under different visual stimulation frequencies in the pre-recorded SSVEP signals. A coupled neural mass model was constructed by establishing connections between occipital and parietal regions, with parameters optimized through particle swarm optimization to accommodate individual differences and neuronal density variations. Experimental results demonstrate that the model achieved a high-precision simulation of real SSVEP signals across multiple stimulation frequencies (10 Hz, 11 Hz, and 12 Hz), with maximum errors decreasing from 2.2861 to 0.8430 as frequency increased. The effectiveness of the model was further validated through the real-time control of an Arduino car, where simulated SSVEP signals were successfully classified by the advanced FPF-net model and mapped to control commands. This research not only advances our understanding of SSVEP neural mechanisms but also releases the user from the brain-controlled coupling system, thus providing a practical framework for developing more efficient and reliable BCI-based systems.