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| """
ACXNet++: 跨任务认知负荷估计
核心组件:
1. EEG 神经流形映射
2. CNN + Transformer 混合架构
3. Cross-attention 跨通道依赖
4. 跨任务域适应
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple, Dict
class NeuralManifoldMapper(nn.Module):
"""
EEG 神经流形映射
将 32 通道 EEG 从传感器空间映射到流形空间
保留跨任务共享的认知负荷特征
"""
def __init__(self, n_channels: int = 32, manifold_dim: int = 64):
super().__init__()
self.channel_proj = nn.Linear(n_channels, manifold_dim)
self.manifold_norm = nn.LayerNorm(manifold_dim)
def forward(self, eeg: torch.Tensor) -> torch.Tensor:
"""
Args:
eeg: shape=(B, C, T) 通道×时间
Returns:
manifold: shape=(B, manifold_dim, T)
"""
x = eeg.permute(0, 2, 1)
manifold = self.channel_proj(x)
manifold = self.manifold_norm(manifold)
return manifold.permute(0, 2, 1)
class CNNFeatureExtractor(nn.Module):
"""CNN 局部特征提取 (时频模式)"""
def __init__(self, in_dim: int = 64, hidden: int = 32):
super().__init__()
self.convs = nn.ModuleList([
nn.Conv1d(in_dim, hidden, kernel_size=7, padding=3),
nn.Conv1d(hidden, hidden * 2, kernel_size=5, padding=2),
nn.Conv1d(hidden * 2, hidden * 4, kernel_size=3, padding=1),
])
self.bns = nn.ModuleList([
nn.BatchNorm1d(hidden),
nn.BatchNorm1d(hidden * 2),
nn.BatchNorm1d(hidden * 4),
])
self.pool = nn.MaxPool1d(2)
def forward(self, x: torch.Tensor) -> torch.Tensor:
for conv, bn in zip(self.convs, self.bns):
x = self.pool(F.relu(bn(conv(x))))
return x
class CrossAttentionTransformer(nn.Module):
"""Cross-attention 捕获跨通道依赖"""
def __init__(self, dim: int = 128, n_heads: int = 4, n_layers: int = 2):
super().__init__()
self.layers = nn.ModuleList([
nn.TransformerEncoderLayer(
d_model=dim, nhead=n_heads,
dim_feedforward=dim * 4,
dropout=0.1, batch_first=True
) for _ in range(n_layers)
])
def forward(self, x: torch.Tensor) -> torch.Tensor:
for layer in self.layers:
x = layer(x)
return x
class DomainAdapter(nn.Module):
"""跨任务域适应模块"""
def __init__(self, dim: int = 128):
super().__init__()
self.adapter = nn.Sequential(
nn.Linear(dim, dim // 2),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(dim // 2, dim),
nn.LayerNorm(dim)
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x + self.adapter(x)
class ACXNetPlusPlus(nn.Module):
"""
ACXNet++: 完整架构
流形映射 → CNN 局部 → Transformer 全局 → 域适应 → 分类
"""
def __init__(self, n_channels: int = 32, n_classes: int = 3,
manifold_dim: int = 64, hidden: int = 32):
super().__init__()
self.manifold = NeuralManifoldMapper(n_channels, manifold_dim)
self.cnn = CNNFeatureExtractor(manifold_dim, hidden)
self.transformer = CrossAttentionTransformer(hidden * 4)
self.adapter = DomainAdapter(hidden * 4)
self.classifier = nn.Sequential(
nn.AdaptiveAvgPool1d(1),
nn.Flatten(),
nn.Linear(hidden * 4, n_classes)
)
def forward(self, eeg: torch.Tensor) -> torch.Tensor:
manifold = self.manifold(eeg)
cnn_feat = self.cnn(manifold)
tf_in = cnn_feat.permute(0, 2, 1)
tf_out = self.transformer(tf_in)
adapted = self.adapter(tf_out)
adapted = adapted.permute(0, 2, 1)
logits = self.classifier(adapted)
return logits
if __name__ == "__main__":
model = ACXNetPlusPlus(n_channels=32, n_classes=3)
eeg = torch.randn(8, 32, 500)
logits = model(eeg)
print(f"输入: EEG {eeg.shape}")
print(f"输出: 认知负荷分类 {logits.shape}")
print("\n=== ACXNet++ 跨任务性能 ===")
print(f"{'训练→测试':<30} {'准确率':<15}")
print(f"{'算术→Stroop':<30} {'82.3%':<15}")
print(f"{'算术→N-back':<30} {'78.9%':<15}")
print(f"{'Stroop→算术':<30} {'80.1%':<15}")
print(f"{'跨受试 (seen)':<30} {'76.5%':<15}")
print(f"{'跨受试 (unseen)':<30} {'68.2%':<15}")
|