中国医疗设备2025，Vol.40Issue(7)：10-14,26,6.DOI:10.3969/j.issn.1674-1633.20240951
基于卷积自注意力机制的KAN神经网络对脑机接口视觉电刺激信号分类
Classification of Visual Electrical Stimulation Signals at Brain Computer Interfaces Using KAN Neural Network Based on Convolutional Self Attention Mechanism
摘要
Abstract
Objective To analyze the numerous challenges encountered in the classification of P300 visual evoked potential signals and to investigate novel prospective solutions.Methods This study introduced a novel KAN neural network model,augmented with a convolutional self-attention mechanism.This innovative approach proficiently captured the global feature information of P300 signals.Furthermore,the incorporation of the KAN layer enhanced its capability to manage nonlinear data effectively.To validate the efficacy of this model,experiments were executed utilizing the widely recognized brain computer interface Competition Ⅲ Challenge 2004 dataset.The performance of our proposed model was subsequently juxtaposed with modern P300 VEP classification techniques.Results The proposed model exhibited superior classification accuracy on the validation set,achieving an impressive 100.0%accuracy in P300 signal classification.This performance surpassed that of VGG-16,which achieved 98.9%,and ResNet-18,which achieved 99.0%.Furthermore,in experiments involving fast gradient sign methed attacks,the model maintained an accuracy of 82%.Conclusion This study presents a novel methodology for the classification of P300 VEP signals,which can also be applied to similar tasks in brain signal processing.This offers fresh research perspectives and contributes to the progression of brain signal research.关键词
卷积自注意力机制/KAN神经网络模型/P300视觉信号/脑机接口/脑电图/非线性数据Key words
vconvolutional self-attention mechanism/KAN neural network model/P300 visual signals/brain computer interface/electroencephalography/nonlinear data分类
医药卫生引用本文复制引用
高健云,刘松丽,李澍..基于卷积自注意力机制的KAN神经网络对脑机接口视觉电刺激信号分类[J].中国医疗设备,2025,40(7):10-14,26,6.基金项目
科技部重点研发计划(2022YFC2409604). （2022YFC2409604）
