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

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.