https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-6279-4446
Tianjin Key Laboratory of Refrigeration Technology, Tianjin University of Commerce, China.
Show others and affiliations
2023 (English)In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 241, article id 110494Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Elsevier Ltd , 2023. Vol. 241, article id 110494
Keywords [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
National Category
Control Engineering
Identifiers
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
Available from: 2023-06-28 Created: 2023-06-28 Last updated: 2023-12-04Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Li, Hailong

Search in DiVA

By author/editor
Li, Hailong
By organisation
Future Energy Center
In the same journal
Building and Environment
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 9 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf