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The use of the general thermal sensation discriminant model based on CNN for room temperature regulation by online brain-computer interface
Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, China.
Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, China.
Mälardalens universitet, Akademin för ekonomi, samhälle och teknik, Framtidens energi.ORCID-id: 0000-0002-6279-4446
Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, China.
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2023 (engelsk)Inngår i: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 241, artikkel-id 110494Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Brain-computer interface (BCI) technology can realize dynamic room temperature adjustment based on individual real-time thermal sensation, which can provide the basis for future intelligent buildings. However, the generalization ability of previous thermal sensation discrimination model (TSDM) is limited, which is a serious obstacle to the application. In this paper, a general TSDM was developed by using convolutional neural network (CNN), which can be well applied to new subjects. In the study, the CNN-TSDM was established and evaluated based on the offline experimental data, and then the BCI closed-loop online room temperature control experiment was carried out based on this CNN-TSDM to further verify. The offline analysis results show that the recognition performance of CNN-TSDM in new subjects is significantly higher than that of typical shallow learning algorithms, and its area under the ROC curve (AUC) value reaches 0.789. In the online experiments of the two simulated environments, BCI using the CNN-TSDM dynamically controlled the air conditioning to improve the room temperature to the comfortable level according to the subjects' thermal sensation. The subjective score of subjects decreased from 3.1 to 3.0 for the hot uncomfortable to 1.1 and 1.2 for the cool comfortable (p < 0.001, p < 0.001). Moreover, in a hotter simulated experimental environment, BCI automatically controlled the air conditioner for longer cooling to obtain a same degree of thermal comfort. The total cooling time (p < 0.05) and the single cooling time (p < 0.05) of the air conditioner were significantly increased. This further confirmed the effectiveness and robustness of the general CNN-TSDM.

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Elsevier Ltd , 2023. Vol. 241, artikkel-id 110494
Emneord [en]
Brain-computer interface (BCI), Convolutional neural network (CNN), Electroencephalogram (EEG), Intelligent building, Thermal comfort, Thermal sensation, Air conditioning, Brain computer interface, Convolution, Cooling, Domestic appliances, Intelligent buildings, Temperature control, Air conditioner, Brain-computer interface, Convolutional neural network, Cooling time, Discriminant models, Discrimination model, Electroencephalogram, Model-based OPC, Thermal sensations, Electroencephalography
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URN: urn:nbn:se:mdh:diva-63667DOI: 10.1016/j.buildenv.2023.110494ISI: 001027060400001Scopus ID: 2-s2.0-85161673060OAI: oai:DiVA.org:mdh-63667DiVA, id: diva2:1776898
Tilgjengelig fra: 2023-06-28 Laget: 2023-06-28 Sist oppdatert: 2023-12-04bibliografisk kontrollert

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