# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 2 Literature Review
### Deep Learning-Based EEG Data Analysis  
\"Deep learning\" is a machine learning technique based on multilayer artificial neural networks, modeled after the structure of the human brain. It excels at learning complex, nonlinear data patterns and is particularly effective for unstructured data such as images, audio, and time series [77]. Since EEG is a complex biological signal, deep learning has recently garnered attention for its capacity to automatically extract meaningful features from such data. Deep learning models comprise an input layer, multiple hidden layers, and an output layer, typically trained via backpropagation algorithms. Optimizers such as stochastic gradient descent, adaptive moments of gradient (Adam), and root-mean-square propagation and regularization methods like dropout and batch normalization are used to prevent overfitting and enhance generalizability [78; 79]. As outlined in Table 6, activation functions provide nonlinear transformations that help learn the complex spatiotemporal patterns in multichannel EEG data. EEG's high temporal resolution and inter-channel spatial correlation increase the difficulty of analysis [80; 81]. Through its layered architecture, deep learning can jointly learn these spatiotemporal characteristics, positioning the method as effective for EEG-based emotion recognition or cognitive state classification [61; 82]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study (Year)** & **Main Topic** & **EEG Setup** & **Primary Conclusion** \\ \hline [PERSON] et al. (2022) [67] & Room size and window placement for cognitive effects & Alpha, beta, delta, and theta (57 channels) & Larger rooms increased alpha power; SVM predicted user responses \\ \hline [PERSON] et al. (2021) [68] & Indoor green walls for stress reduction & Relative alpha, theta, and beta (O, P) & Green walls mitigated stress; alpha wave levels rose with relaxation \\ \hline [PERSON] and [PERSON] (2021) [69] & Aesthetic satisfaction, resilience, anxiety & Frontal/occipital (14 channels) & EEG effectively classified emotional states; frontal area central for evaluation \\ \hline [PERSON] et al. (2022) [70] & Cognitive responses in VR versus identical real spaces & Delta, theta, alpha, beta, and gamma & VR elicited cognitive patterns similar to those in real environments \\ \hline [PERSON] et al. (2022) [71] & Influence of wall color on attention and memory & C3, Cz, F3, and Fz (beta, high beta) & Cool-colored classrooms boosted arousal, attention, and memory \\ \hline [PERSON] et al. (2021) [72] & Impact of design elements on wayfinding & Delta–gamma & Improved features enhanced spatial orientation and cognitive engagement \\ \hline [PERSON] et al. (2023) [73] & Room size and color in brain connectivity & Theta and gamma (right frontal/temporal) & Smaller rooms plus blue color, shifted gamma/theta connectivity patterns \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Different deep learning models are applied depending on data characteristics and analytical objectives. CNNs excel at extracting spatial features in multichannel EEG data but may be limited in modeling temporal dependencies. In contrast, recurrent neural networks and variants like LSTM are better at handling time-series continuity but can be computationally expensive [77; 83; 84; 85]. Gated recurrent units offer a simpler structure than LSTM, reducing computational load, whereas the transformer model leverages parallel processing and attention mechanisms for large-scale data learning but demands significant computational resources [78; 86]. Originally praised for their performance in image processing, CNNs have shown notable success in detecting spatial patterns across multiple EEG channels [87; 88; 89]. The convolutional layers in a CNN capture local features in multichannel EEG data, while pooling layers reduce spatial dimensions to mitigate noise and improve computational efficiency [77; 88].