基于改进卷积神经网络的SSVEP解析算法

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中图分类号：TP391 文献标志码：A 文章编号：1000-1565（2025）05-0530-11

Analytical algorithm of SSVEP based on improved convolutional neural network

YANG Jianli1’²，ZHAO Songlei1，LIU Fengshuang¹，YANG Xiaoru，ZHANG Shuo4 i1. College of Electronic and Information Engineering，Hebei University， Baoding O7lo02，China; 2.Key Laboratory of Digital Medical Engineering of Hebei Province，Baoding O710o2，China; 3. Productivity Promotion Center of Baoding，Baoding O71ooo，China;4. Human Resources Department，Affiliated Hospital of Hebei University，Baoding O7looo，China）

Abstract： Steady-state visual evoked potentials（SSVEP） isa commonly used modality in braincomputer interfaces，which usually suffers from insufficient utilization of time domain and frequency information， resulting in imprecise and untimely signal resolution. For this reason，this paper proposes an improved SSVEP analysis algorithm for convolutional neural network model. A multi-channel input model is designed. With multiple frequency band filtered signals as input，the depth features of time domain and frequency domain are extracted by parallel time attention module and multi-band combination module.The classification module realizes the accurate analysis of SSVEP signal through the fusion analysis of multifeature domain. The proposed algorithm is verified on two common data sets，and the clasification accuracy is 98.14% and 82.72% ，respectively. The experimental results show that the model exhibits high performance and robustness，thereby facilitating the development of brain-computer interface based on SSVEP.

Key words：convolutional neural network;brain-computer interface;SSVEP

基于脑电信号的脑机接口（brain computer interface，BCI）通过对脑电信号进行非侵入性测量提取出特定的特征，从而转化为设备的控制指令[1]．BCI通过一种新型的无接触的通信通道来控制外部设备，实现人脑与机器的智能交互，从而辅助残疾人的生活[2-3]，甚至控制电脑游戏[4]和智能家居[5]．在不同的基于脑电信号的 BCI中，如运动想象和事件相关电位等，稳态视觉诱发电位（steady-state visual evoked potential，SS-VEP）具有信息传输率（information transferrate，ITR）高、训练时间短等优点，因此广泛应用于人机交互领域[6-7]。（剩余14883字）