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- W4313594908 endingPage "101868" @default.
- W4313594908 startingPage "101868" @default.
- W4313594908 abstract "Accurate classification of human emotions in designed spaces is essential for architects and engineers, who aim to maximize positive emotions by configuring architectural design features. Previous studies at the conjunction of neuroscience and architecture confirmed the impact of architectural design features on human emotions. Recent development of biometric sensors enabled researchers to identify emotions by measuring human physiological responses (e.g., the use of electroencephalogram (EEG) to measure brain activities). However, a gap in the knowledge exists in terms of an accurate classification model for human emotions in design variants. This study proposed a convolutional neural network (CNN) based approach to classify human emotions. The approach considered two types of CNN architectures as CNN ensemble and auto-encoders. The inputs of these CNN algorithms were 2D images generated by projecting the frequency band power of EEG onto the scalp graph in accordance with the electrode placements. This transformation from time-series EEG data to 2D frequency band power images retain the spatial, time and frequency domain features from participants’ brain dynamics. Performance of the proposed approach was validated using multiple metrics, including precision, recall, f-1 score, and Area Under Curve (AUC). Results showed that the auto-encoder based approach achieved the best performance with an AUC of 0.95." @default.
- W4313594908 created "2023-01-06" @default.
- W4313594908 creator A5001924431 @default.
- W4313594908 creator A5065463464 @default.
- W4313594908 date "2023-01-01" @default.
- W4313594908 modified "2023-09-26" @default.
- W4313594908 title "Towards emotionally intelligent buildings: A Convolutional neural network based approach to classify human emotional experience in virtual built environments" @default.
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- W4313594908 cites W1997333298 @default.
- W4313594908 cites W2004204487 @default.
- W4313594908 cites W2005099568 @default.
- W4313594908 cites W2045312759 @default.
- W4313594908 cites W2045468728 @default.
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- W4313594908 cites W2159200188 @default.
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- W4313594908 doi "https://doi.org/10.1016/j.aei.2022.101868" @default.
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