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| import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
"""
EEG 基础模型跨被试泛化评估框架
参考 EEG-FM-Compass (arXiv:2601.17883)
用于评估 EEG 基础模型在驾驶员认知分心检测中的跨被试迁移能力
"""
class EEGDataset(Dataset):
"""EEG 数据集，支持 LOSO 交叉验证"""
def __init__(self, eeg_data: np.ndarray, labels: np.ndarray,
subject_ids: np.ndarray, task: str = "cognitive_load"):
"""
Args:
eeg_data: EEG 信号, shape=(N, C, T) N=样本数, C=通道数, T=时间点
labels: 标签 (0=正常, 1=认知分心)
subject_ids: 被试ID, shape=(N,)
task: 任务类型
"""
self.data = torch.FloatTensor(eeg_data)
self.labels = torch.LongTensor(labels)
self.subject_ids = subject_ids
self.task = task
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return {
'eeg': self.data[idx],
'label': self.labels[idx],
'subject_id': self.subject_ids[idx]
}
class EEGTransformerEncoder(nn.Module):
"""
轻量级 EEG Transformer 编码器
参考 LaBraM 架构，适配驾驶员认知分心检测
输入: (B, C, T) B=batch, C=通道数(如19), T=时间点(如256, 2s@128Hz)
输出: (B, num_classes) 认知状态分类
"""
def __init__(self,
num_channels: int = 19,
seq_len: int = 256,
embed_dim: int = 256,
num_heads: int = 8,
num_layers: int = 6,
num_classes: int = 2,
patch_size: int = 16):
super().__init__()
self.num_channels = num_channels
self.patch_size = patch_size
self.num_patches = seq_len // patch_size
self.channel_embed = nn.Embedding(num_channels, embed_dim)
self.patch_embed = nn.Conv1d(
in_channels=1,
out_channels=embed_dim,
kernel_size=patch_size,
stride=patch_size
)
self.pos_embed = nn.Parameter(
torch.randn(1, num_channels * self.num_patches, embed_dim) * 0.02
)
encoder_layer = nn.TransformerEncoderLayer(
d_model=embed_dim,
nhead=num_heads,
dim_feedforward=embed_dim * 4,
dropout=0.1,
batch_first=True,
activation='gelu'
)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
self.norm = nn.LayerNorm(embed_dim)
self.classifier = nn.Linear(embed_dim, num_classes)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x: (B, C, T) EEG 信号
Returns:
logits: (B, num_classes)
"""
B, C, T = x.shape
x = x.unsqueeze(2)
x = x.view(B * C, 1, T)
x = self.patch_embed(x)
x = x.transpose(1, 2)
ch_ids = torch.arange(C, device=x.device).unsqueeze(0).repeat(B, 1)
ch_emb = self.channel_embed(ch_ids.view(-1))
ch_emb = ch_emb.view(B, C, 1, -1).repeat(1, 1, self.num_patches, 1)
ch_emb = ch_emb.view(B * C, self.num_patches, -1)
x = x + ch_emb
x = x + self.pos_embed
x = self.transformer(x)
x = self.norm(x)
x = x.mean(dim=1)
x = x.view(B, C, -1).mean(dim=1)
logits = self.classifier(x)
return logits
class ProgressiveUnfreezing:
"""
渐进式解冻策略
参考 Fonya et al. (arXiv:2606.23706)
逐步解冻 Transformer 层，避免灾难性遗忘
"""
def __init__(self, model: nn.Module, total_epochs: int = 50,
unfreeze_schedule: list = None):
self.model = model
self.total_epochs = total_epochs
self.schedule = unfreeze_schedule or list(range(0, total_epochs, 10))
def apply_freeze(self, epoch: int):
"""根据当前 epoch 冻结/解冻对应层"""
layers_to_unfreeze = sum(1 for s in self.schedule if s <= epoch)
for param in self.model.transformer.parameters():
param.requires_grad = False
for i in range(min(layers_to_unfreeze, len(self.model.transformer.layers))):
layer = self.model.transformer.layers[-(i+1)]
for param in layer.parameters():
param.requires_grad = True
for param in self.model.classifier.parameters():
param.requires_grad = True
param.data.normal_(0, 0.01)
for param in self.model.norm.parameters():
param.requires_grad = True
def evaluate_cross_subject(model, dataset, subject_ids):
"""
留一被试交叉验证（LOSO）
Args:
model: EEG 模型
dataset: 完整数据集
subject_ids: 所有被试 ID
Returns:
per_subject_acc: 每个被试的准确率
"""
from sklearn.metrics import accuracy_score, f1_score
results = {}
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
for test_subj in np.unique(subject_ids):
train_mask = subject_ids != test_subj
test_mask = subject_ids == test_subj
train_data = dataset.data[train_mask]
train_labels = dataset.labels[train_mask]
test_data = dataset.data[test_mask]
test_labels = dataset.labels[test_mask]
train_ds = torch.utils.data.TensorDataset(train_data, train_labels)
test_ds = torch.utils.data.TensorDataset(test_data, test_labels)
train_loader = DataLoader(train_ds, batch_size=64, shuffle=True)
test_loader = DataLoader(test_ds, batch_size=64)
model.apply(lambda m: m.reset_parameters() if hasattr(m, 'reset_parameters') else None)
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4, weight_decay=0.01)
criterion = nn.CrossEntropyLoss()
unfreezer = ProgressiveUnfreezing(model, total_epochs=50)
for epoch in range(50):
model.train()
unfreezer.apply_freeze(epoch)
for batch_x, batch_y in train_loader:
batch_x, batch_y = batch_x.to(device), batch_y.to(device)
logits = model(batch_x)
loss = criterion(logits, batch_y)
optimizer.step()
optimizer.zero_grad()
loss.backward()
model.eval()
all_preds = []
with torch.no_grad():
for batch_x, _ in test_loader:
batch_x = batch_x.to(device)
logits = model(batch_x)
all_preds.extend(logits.argmax(dim=1).cpu().numpy())
acc = accuracy_score(test_labels.numpy(), all_preds)
f1 = f1_score(test_labels.numpy(), all_preds, average='macro')
results[f'subject_{test_subj}'] = {'accuracy': acc, 'f1': f1}
return results
if __name__ == "__main__":
np.random.seed(42)
torch.manual_seed(42)
num_subjects = 10
samples_per_subject = 50
num_channels = 19
seq_len = 256
all_data = []
all_labels = []
all_subjects = []
for subj_id in range(num_subjects):
normal_data = np.random.randn(samples_per_subject // 2, num_channels, seq_len) * 0.5 + 0.3
normal_labels = np.zeros(samples_per_subject // 2, dtype=int)
distracted_data = np.random.randn(samples_per_subject // 2, num_channels, seq_len) * 0.5 + 0.3
t = np.arange(seq_len) / 128
alpha_wave = np.sin(2 * np.pi * 10 * t)
distracted_data += alpha_wave * 0.3
all_data.extend([normal_data, distracted_data])
all_labels.extend([normal_labels, np.ones(samples_per_subject // 2, dtype=int)])
all_subjects.extend([subj_id] * samples_per_subject)
eeg_data = np.concatenate(all_data, axis=0)
labels = np.concatenate(all_labels, axis=0)
subject_ids = np.array(all_subjects)
print(f"数据集: {eeg_data.shape[0]} 样本, {num_channels} 通道, {seq_len} 时间点")
print(f"被试数: {num_subjects}")
dataset = EEGDataset(eeg_data, labels, subject_ids)
model = EEGTransformerEncoder(
num_channels=num_channels,
seq_len=seq_len,
embed_dim=256,
num_heads=8,
num_layers=6,
num_classes=2,
patch_size=16
)
print(f"\n模型参数量: {sum(p.numel() for p in model.parameters()):,}")
results = evaluate_cross_subject(model, dataset, subject_ids)
print("\n===== 跨被试 LOSO 评估结果 =====")
accs = []
for subj, metrics in results.items():
print(f" {subj}: Acc={metrics['accuracy']:.4f}, F1={metrics['f1']:.4f}")
accs.append(metrics['accuracy'])
print(f"\n平均准确率: {np.mean(accs):.4f} ± {np.std(accs):.4f}")
print(f"最佳被试: {max(accs):.4f}")
print(f"最差被试: {min(accs):.4f}")
|