This study aims to develop an automated classification of indoor air quality control using the machine learning based on an electroencephalogram (EEG) signal. Two experiments were conducted: The first aimed to select an EEG channel based on the brain wave indices when the indoor environmental quality (IEQ) changes. We then extract the brain wave indices’ feature when the subjects conduct adaptive behaviors and predict the IEQ condition control using machine learning including the stacked ensembles. The extracted features were classified using base estimators such as distributed random forest, gradient boosting machine (GBM), generalized linear models (GLMs), deep neural network, and new predicted data were retrained and predicted by metalearner (i.e., GLM). In Dimension 1, the air conditioning system and the air ventilation system, and the area under curve (AUC) of the proposed stacked ensembles trained by base estimators was the highest, 0.9038. In Dimension 2, turning on and turning off, the AUC of the GBM is the highest, 0.8384. Based on these results, the EEG signal can be used to suggest an automatic IEQ control model that can then reduce the drowsiness, and increase attention.

中文翻译：

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基于脑电图的堆叠式集成体对室内环境质量控制的自动分类

这项研究旨在利用基于脑电图（EEG）信号的机器学习来开发室内空气质量控制的自动分类。进行了两个实验：第一个实验旨在在室内环境质量（IEQ）变化时根据脑电波指数选择EEG通道。然后，当受试者进行适应性行为时，我们将提取脑电波指数特征，并使用包括堆叠式集成体的机器学习来预测IEQ条件控制。使用基本估计量对提取的特征进行分类，例如分布式随机森林，梯度提升机（GBM），广义线性模型（GLM），深度神经网络，以及新的预测数据由Metalearner（即GLM）进行重新训练和预测。在维度1中，空调系统和空气通风系统，而由基本估算器训练的拟议叠置集成的曲线下面积（AUC）最高，为0.9038。在维度2中，打开和关闭，GBM的AUC最高，为0.8384。基于这些结果，EEG信号可用于建议自动IEQ控制模型，从而减少睡意并增加注意力。