内蒙古民族大学学报（自然科学版）2025，Vol.40Issue(2)：59-68,10.DOI:10.14045/j.cnki.15-1220.2025.02.009
基于脑电图的自注意力卷积神经网络酒瘾检测模型
A Convolutional Neural Networks Model for Alcohol Addiction Detection Based on EEG and Self-attention Mechanisms
摘要
Abstract
Alcohol addiction is currently mainly diagnosed by doctors based on experience,which is highly sub-jective.The use of electroencephalography(EEG)to detect alcohol addiction can assist doctors in making objective judgments.This study proposes an alcohol addiction detection CNN model AADNet based on attention mechanism.AADNet consists of a convolution module,a self-attention module,a feature enhancement module and a classifica-tion module.The convolution module extracts the local features of EEG signals through spatial convolution and tem-poral convolution.The self-attention module extracts the global features of EEG signals through the spatial self-at-tention mechanism and the feature self-attention mechanism.The feature enhancement module further fuses local features and global features to extract features strongly related to categories.The classification module is responsi-ble for predicting the probability of alcohol addiction.The experimental results show that the proposed model can ef-fectively detect alcohol addiction,and the accuracy on the public data set can reach 100.00%,which is better than most of the current algorithms.关键词
脑电图/酒精成瘾/自注意力机制/特征增强Key words
EEG/alcohol addiction/self-attention mechanisms/feature enhancement分类
电子信息工程引用本文复制引用
张东晓,王佳毅,叶贵,马素凡..基于脑电图的自注意力卷积神经网络酒瘾检测模型[J].内蒙古民族大学学报（自然科学版）,2025,40(2):59-68,10.基金项目
国家自然科学基金项目(12271211) （12271211）
福建省科技创新智库课题研究项目(FJKX-2023XKB007) （FJKX-2023XKB007）
