轻量级动态脑连接框架方法论。基于时变有向传递函数（TV-DTF）估计脑区间方向性信息流，用于压力分类和脑网络分析。适用于EEG压力检测、动态功能连接、因果脑网络分析。触发词：动态脑连接、TV-DTF、压力检测、方向性信息流、脑网络、dynamic connectivity、stress detection、directed transfer function。
通过时变有向传递函数（TV-DTF）估计脑区间方向性信息流，实现轻量级动态脑网络分析，用于压力状态分类。
来源： arXiv:2511.05505 效用： 1.0
时变有向传递函数（Time-Varying Directed Transfer Function）：
数学表达：
TV-DTF_ij(f,t) = |H_ij(f,t)|² / Σ_k|H_ik(f,t)|²
其中 H 为传递函数矩阵。
步骤：
关键频带：
分类器对比：
| 分类器 | 2类准确率 | 3类准确率 |
|---|---|---|
| SVM | — | 89.73% |
| XGBoost | 93.69% | — |
| Random Forest | 高 | 高 |
关键发现：
import numpy as np
from scipy import signal
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
import xgboost as xgb
class TVDTFClassifier:
"""时变有向传递函数压力分类器"""
def __init__(self, fs=500, n_channels=32):
self.fs = fs
self.n_channels = n_channels
self.freq_bands = {
'delta': (1, 4),
'theta': (4, 8),
'alpha': (8, 12),
'beta': (12, 30),
'gamma': (30, 45)
}
def compute_tv_dtf(self, eeg_data, band_name):
"""
计算特定频带的TV-DTF
Parameters:
-----------
eeg_data : np.ndarray, shape (n_channels, n_samples)
多通道EEG数据
band_name : str
频带名称
Returns:
--------
dtf_matrix : np.ndarray, shape (n_channels, n_channels)
方向性连接矩阵
"""
# 带通滤波
fmin, fmax = self.freq_bands[band_name]
filtered = self._bandpass_filter(eeg_data, fmin, fmax)
# 计算传递函数（简化版本）
# 实际实现需要多变量自回归模型
dtf_matrix = self._estimate_dtf(filtered)
return dtf_matrix
def _bandpass_filter(self, data, fmin, fmax):
"""带通滤波"""
b, a = signal.butter(4,
[fmin/(self.fs/2), fmax/(self.fs/2)],
btype='band')
return signal.filtfilt(b, a, data, axis=1)
def _estimate_dtf(self, data):
"""估计DTF矩阵（简化）"""
n_ch = data.shape[0]
dtf = np.zeros((n_ch, n_ch))
# 使用格兰杰因果简化估计
for i in range(n_ch):
for j in range(n_ch):
if i != j:
# 计算方向性影响
dtf[i, j] = self._granger_causality(data[j], data[i])
# 归一化
row_sums = dtf.sum(axis=1, keepdims=True)
dtf_normalized = dtf / (row_sums + 1e-10)
return dtf_normalized
def _granger_causality(self, x, y):
"""简化的格兰杰因果检验"""
# 使用互相关作为简化度量
corr = np.abs(signal.correlate(x, y, mode='valid'))
return np.max(corr)
def extract_features(self, eeg_data):
"""提取TV-DTF特征"""
features = []
# 提取Alpha和Beta频带特征（最强判别力）
for band in ['alpha', 'beta']:
dtf = self.compute_tv_dtf(eeg_data, band)
# 展平上三角矩阵
upper_tri = dtf[np.triu_indices(self.n_channels, k=1)]
features.extend(upper_tri)
return np.array(features)
def fit(self, X, y):
"""训练XGBoost分类器"""
self.clf = xgb.XGBClassifier(
n_estimators=100,
max_depth=6,
learning_rate=0.1
)
self.clf.fit(X, y)
def predict(self, X):
"""预测压力状态"""
return self.clf.predict(X)
SAM 40 数据集：
| 参数 | 推荐值 | 说明 |
|---|---|---|
| 通道数 | 32 | 标准EEG配置 |
| 采样率 | 500 Hz | 时变分析精度 |
| Alpha频带 | 8-12 Hz | 最强判别力 |
| Beta频带 | 12-30 Hz | 次强判别力 |
| 分类器 | XGBoost/SVM | 最优性能 |
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