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| import numpy as np
class AviationMultiModalAnalysis:
"""
航空多模态信号分析框架
参考: Frontiers in Neuroergonomics 2026
核心: 评估各信号在真实飞行中的构念相关性
"""
def __init__(self):
self.construct_relevance = {
'eeg_alpha': {'cognitive_load': 0.75, 'artifact_risk': 0.45},
'eeg_beta': {'cognitive_load': 0.80, 'artifact_risk': 0.50},
'hrv_rmssd': {'stress_load': 0.70, 'artifact_risk': 0.15},
'eda_scl': {'arousal': 0.65, 'artifact_risk': 0.30},
'gaze_entropy': {'attention': 0.85, 'artifact_risk': 0.25},
'fixation_duration': {'attention': 0.80, 'artifact_risk': 0.20},
'skin_temp': {'autonomic': 0.45, 'artifact_risk': 0.10},
'flight_dynamics': {'task_load': 0.90, 'artifact_risk': 0.05},
}
def evaluate_signal_quality(self, signal_name: str) -> dict:
"""评估信号在飞行环境中的质量"""
if signal_name not in self.construct_relevance:
return {'quality': 'unknown'}
info = self.construct_relevance[signal_name]
snr = info.get('cognitive_load', info.get('stress_load',
info.get('arousal', info.get('attention',
info.get('autonomic', info.get('task_load', 0.5)))))) / \
max(info['artifact_risk'], 0.05)
if snr > 3.0:
quality = 'excellent'
elif snr > 2.0:
quality = 'good'
elif snr > 1.0:
quality = 'marginal'
else:
quality = 'poor'
return {
'quality': quality,
'snr': snr,
'construct_relevance': {k: v for k, v in info.items() if k != 'artifact_risk'},
'artifact_risk': info['artifact_risk'],
}
def recommend_automotive_transfer(self) -> list:
"""推荐可迁移到汽车座舱的信号"""
recommendations = []
for signal, info in self.construct_relevance.items():
auto_artifact = info['artifact_risk'] * 0.5
for construct, relevance in info.items():
if construct == 'artifact_risk':
continue
if relevance > 0.6 and auto_artifact < 0.25:
recommendations.append({
'signal': signal,
'construct': construct,
'aviation_relevance': relevance,
'auto_relevance': relevance * 1.1,
'auto_artifact': auto_artifact,
'transferable': True,
})
recommendations.sort(key=lambda x: x['auto_relevance'], reverse=True)
return recommendations
if __name__ == "__main__":
analysis = AviationMultiModalAnalysis()
print("=== 航空多模态信号质量评估 ===\n")
for signal in analysis.construct_relevance:
result = analysis.evaluate_signal_quality(signal)
print(f"{signal}: {result['quality']} (SNR={result['snr']:.2f})")
print("\n=== 可迁移到汽车座舱的信号 ===\n")
transferable = analysis.recommend_automotive_transfer()
for t in transferable:
print(f"{t['signal']} → {t['construct']}: "
f"航空={t['aviation_relevance']:.2f}, "
f"汽车={t['auto_relevance']:.2f}, "
f"伪影={t['auto_artifact']:.2f}")
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