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Indoor Light Environment Comfort Evaluation Method Based on Deep Learning and Evoked Potentials

Year: 2025

Type: article

Abstract: The optimal indoor lighting comfort can enhance physical and mental health and improve work efficiency. The traditional methods for evaluating lighting comfort have problems such as limited data analysis and poor subjectivity. To establish objective criteria, this study proposes a novel method combining deep learning and evoked potentials. This study collected visual evoked potentials across diverse indoor lighting conditions and employed Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) recurrent neural networks to classify temporal evoked electroencephalography data. The experimental results show that both LSTM and GRU achieve higher accuracy than the Feedforward Neural Network. Among them, LSTM performs best, reaching an accuracy of 80.16% while maintaining computational efficiency comparable to GRU. Such effective objective evaluation methods provide a scientific basis for optimizing indoor environments.

Source: Buildings

DOI: 10.3390/buildings15244571

Authors: Sheng Miao, Sudong Li, Xixin Yang, Hongyu Guan, Xiang Shen

Institutions: Qingdao University of Technology, Qingdao University, George Washington University

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,137