双层GCN-LSTM网络对通道选择后的脑电数据以及邻接矩阵进行训练和情绪分类的代码

双层 GCN (图卷积神经网络) 和 LSTM (长短时记忆网络) 结合用于脑电数据的情绪分类通常涉及深度学习和信号处理领域。以下是基本步骤和代码框架的一个概述：

import torch
import torch.nn as nn
from torch_geometric.nn import GCNConv
from torch.nn.utils.rnn import LSTMCell

class GCNLSTMModel(nn.Module):
    def __init__(self, input_channels, hidden_channels, num_classes):
        super(GCNLSTMModel, self).__init__()
        
        # 创建 GCN 层
        self.conv1 = GCNConv(input_channels, hidden_channels)
        self.conv2 = GCNConv(hidden_channels, hidden_channels)

        # 创建 LSTM 层
        self.lstm = LSTMCell(hidden_channels, hidden_channels)

        # 输出层
        self.fc = nn.Linear(hidden_channels, num_classes)
        self.dropout = nn.Dropout(p=0.5)

    def forward(self, x, edge_index, edge_weight=None):
        # GCN 层应用
        x = self.conv1(x, edge_index, edge_weight)
        x = F.relu(x)
        x = self.conv2(x, edge_index, edge_weight)
        x = F.relu(x)

        # 将图嵌入转换为序列输入 LSTM
        seq_len = x.size(0)
        packed_x = torch.nn.utils.rnn.pack_padded_sequence(x.view(seq_len, -1), padding_value=0)

        # LSTM 遍历
        h, _ = self.lstm(packed_x.data, None)  # 使用None表示隐状态初始化为全零
        
        # 解包并取最后一个时间步作为最终特征
        x, _ = torch.nn.utils.rnn.pad_packed_sequence(h, padding_value=0)
        x = x[-1]

        # dropout 和全连接层
        x = self.dropout(x)
        out = self.fc(x)
        return out

# 示例用法
model = GCNLSTMModel(input_channels, hidden_channels, num_classes)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(num_epochs):
    optimizer.zero_grad()
    
    output = model(data.x, data.edge_index, data.edge_weight)
    loss = F.cross_entropy(output, data.y)
    loss.backward()
    optimizer.step()

    print(f"Epoch {epoch+1}, Loss: {loss.item()}")