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| import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
class TimeResolvedMVPA:
"""
时间分辨多变量模式分析
论文核心方法：在毫秒级时间精度上解码认知负荷状态
使用滑动窗口对每个时间点训练分类器
"""
def __init__(self, n_timepoints: int = 500,
window_ms: int = 100,
step_ms: int = 10):
"""
Args:
n_timepoints: 总时间点数（采样率×时长）
window_ms: 解码窗口长度（毫秒）
step_ms: 步长（毫秒）
"""
self.window_samples = int(window_ms * 500 / 1000)
self.step_samples = int(step_ms * 500 / 1000)
self.classifiers = []
self.time_centers = []
def fit(self, eeg_data: np.ndarray, labels: np.ndarray):
"""
训练时序解码器
Args:
eeg_data: [n_trials, n_timepoints] EEG信号
labels: [n_trials] 0=低负荷, 1=高负荷
Returns:
self
"""
n_trials, n_timepoints = eeg_data.shape
for t in range(0, n_timepoints - self.window_samples,
self.step_samples):
window = eeg_data[:, t:t+self.window_samples]
features = self._extract_features(window)
clf = LogisticRegression(
C=1.0, penalty='l2', solver='lbfgs',
max_iter=1000
)
clf.fit(features, labels)
self.classifiers.append(clf)
self.time_centers.append(t + self.window_samples // 2)
return self
def _extract_features(self, window: np.ndarray) -> np.ndarray:
"""
从EEG窗口提取特征
Args:
window: [n_trials, n_samples]
Returns:
features: [n_trials, n_features]
"""
mean = np.mean(window, axis=1, keepdims=True)
std = np.std(window, axis=1, keepdims=True)
skew = self._skewness(window)
fft = np.fft.rfft(window, axis=1)
power = np.abs(fft) ** 2
freqs = np.fft.rfftfreq(window.shape[1], d=1/500)
theta_mask = (freqs >= 4) & (freqs <= 8)
theta_power = np.sum(power[:, theta_mask], axis=1, keepdims=True)
alpha_mask = (freqs >= 8) & (freqs <= 13)
alpha_power = np.sum(power[:, alpha_mask], axis=1, keepdims=True)
beta_mask = (freqs >= 13) & (freqs <= 30)
beta_power = np.sum(power[:, beta_mask], axis=1, keepdims=True)
theta_alpha_ratio = theta_power / (alpha_power + 1e-8)
features = np.hstack([
mean, std, skew,
theta_power, alpha_power, beta_power,
theta_alpha_ratio
])
return features
def _skewness(self, x: np.ndarray) -> np.ndarray:
"""计算偏度"""
mean = np.mean(x, axis=1, keepdims=True)
std = np.std(x, axis=1, keepdims=True)
return np.mean(((x - mean) / (std + 1e-8)) ** 3,
axis=1, keepdims=True)
def decode_accuracy(self, eeg_data: np.ndarray,
labels: np.ndarray) -> list:
"""
计算每个时间点的解码准确率
Returns:
accuracies: 每个时间点的解码准确率列表
"""
accuracies = []
for i, clf in enumerate(self.classifiers):
t = i * self.step_samples
window = eeg_data[:, t:t+self.window_samples]
features = self._extract_features(window)
acc = cross_val_score(clf, features, labels,
cv=5, scoring='accuracy').mean()
accuracies.append(acc)
return accuracies
if __name__ == "__main__":
np.random.seed(42)
n_trials = 100
n_timepoints = 500 * 30
low_load = np.random.randn(n_trials//2, n_timepoints) * 0.5
low_load += 2.0 * np.sin(2 * np.pi * 10 *
np.arange(n_timepoints) / 500)
high_load = np.random.randn(n_trials//2, n_timepoints) * 0.7
high_load += 3.0 * np.sin(2 * np.pi * 6 *
np.arange(n_timepoints) / 500)
eeg_data = np.vstack([low_load, high_load])
labels = np.array([0]*(n_trials//2) + [1]*(n_trials//2))
decoder = TimeResolvedMVPA(
n_timepoints=n_timepoints,
window_ms=100, step_ms=50
)
decoder.fit(eeg_data, labels)
accuracies = decoder.decode_accuracy(eeg_data, labels)
print(f"时间点数: {len(accuracies)}")
print(f"峰值准确率: {max(accuracies):.2%}")
print(f"平均准确率: {np.mean(accuracies):.2%}")
print(f"检测延迟(首次>55%): {next((i*50 for i, a in enumerate(accuracies) if a > 0.55), 'N/A')}ms")
|