脑电情绪识别双模模型:RNN处理时序特征 + CNN提取空间图像特征(含SEED/DEAP/SEED-IV数据与完整代码)

上传者: qqq44 | 上传时间: 2026-09-28 20:58:17 | 文件大小: 10.19MB | 文件类型: ZIP
这个资源包提供了一个融合RNN与CNN的脑电情绪识别实现方案,专为处理多通道脑电信号设计。核心思路是把脑电数据同时建模为时间序列和二维特征图:RNN部分按通道顺序建模频带功率随时间变化的动态特性,CNN部分则将通道拓扑或特征矩阵转为图像形式,捕获空间分布模式。模型内置显著性引导的信息融合机制,支持联合优化两个分支。代码已适配四个主流公开数据集——SEED、SEED-IV、DEAP和MPED,并附带各数据集对应的电极位置映射文件(Neuro_loc_*.npy)、预处理后的训练数组(img.npy、array.npy、label.npy等)、完整模型定义(Models.py、Sal_Model.py等)、工具函数(Utils.py)、损失函数实现及环境配置(req.txt、env.yml)。PDF论文详细说明了模型结构、实验设置与跨数据集性能对比,Model.png直观展示整体框架,Example_Input提供标准输入格式参考。所有模块均可直接运行,支持快速复现与二次开发。

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