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照亮试点心理状态的神经景观:一个卷积神经网络方法与沙普利的附加解释解释性解释性

Ibrahim Alreshidi^1,2,3, Desmond Bisandu^1,2, Irene Moulitsas^1,2

¹Centre for Computational Engineering Sciences, Cranfield University, Cranfield MK43 0AL, UK.

Sensors (Basel, Switzerland)

|November 25, 2023

概括

这项研究开发了一种可解释的人工智能模型,使用脑电图 (EEG) 数据准确检测飞行员的心理状态,提高航空安全. 该模型在识别诸如道化注意力和正常功能等状态时达到96%的准确性.

科学领域:

神经科学是一个神经科学.
人工智能的人工智能
航空心理学航空心理学

背景情况:

预测飞行员的心理状态对于航空安全和性能至关重要.
脑电图 (EEG) 数据为精神状态的检测提供了潜力.
对于EEG分析的机器学习模型的解释性仍然是一个挑战.

研究的目的:

开发一个可解释的模型,用EEG数据检测四个试点精神状态.
为了增强对飞行员精神状态背后的神经机制的理解.
通过准确和可解释的心理状态监测来提高航空安全.

主要方法:

使用了17名飞行员的脑电图 (EEG) 数据.
在功率光谱密度特征上训练了一个卷积神经网络 (CNN).
为了模型的可解释性,使用了夏普利添加式解释 (SHAP).

主要成果:

在所有指标上实现了高性能:96%的平均准确率,96%的精度,94%的回忆率和95%的F1得分.
通过使用SHAP值,确定了每个精神状态的十大影响力EEG特征.
在精神状态之间显示出EEG频段的显著差异.

结论:

开发的可解释模型有效地以高准确度检测飞行员的心理状态.
SHAP分析提供了对不同心理状态的神经相关的见解.
这种方法促进了航空安全的基于EEG的精神状态检测.

关键词:

这是一个EEGEEGEEGEEGEEGEEGEEG.莎普利的添加式解释航空安全航空安全.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.一个电脑电图 (electroencephalogram) 是一个电脑电图.可解释性/可解释性机器学习是机器学习.心理状态分类心理状态分类飞行员的缺点飞行员的缺点

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