# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 2 Literature Review
### Deep Learning-Based EEG Data Analysis
Meanwhile, LSTM, a specialized recurrent neural network architecture, addresses the challenge of long-term dependencies in sequential EEG data [85; 90; 91]. LSTM alleviates vanishing or exploding gradients by incorporating input, forget, and output gates, making it particularly effective for ERP interval analysis or irregular EEG pattern detection [92; 93; 94; 95]. Recently, hybrid CNN-LSTM approaches, which combine the ability of CNNs to extract spatial features with the ability of LSTM to capture temporal dependencies, have shown a superior accuracy and generalization over single-model alternatives [96]. Table 7 briefly compares CNN, LSTM, and CNN-LSTM models, while Figure 1 visualizes an example CNN-LSTM architecture [96; 97]. Empirical studies show EEG-based deep learning models excel in various tasks, including emotion recognition, motor imagery classification, seizure detection, and sleep stage classification, among others [92; 99; 100; 101; 102]. Table 8 summarizes key studies that achieved a circa 85-90% or higher accuracy in these tasks [93; 99; 103; 104; 105; 106]. CNN-LSTM hybrid models have recorded over 90% accuracy in emotion recognition, significantly expanding the potential of EEG-based brain-computer interface applications [107; 102]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **Equation** & **Output Range** & **Characteristics** & **Common Usage** & **Limitations** \\ \hline \multirow{4}{*}{Sigmoid} & \multirow{4}{*}{\(\sigma(x)=\frac{1}{1+e^{-x}}\)} & \multirow{4}{*}{(0, 1)} & Good for binary classification; may saturate gradients & Output layer (binary) & Highly sensitive to artifacts from blinking, muscle movements, and electrical interference \\ \hline \multirow{2}{*}{Tanh} & \multirow{2}{*}{\(tanh(x)=2\sigma(2x)-1\)} & \multirow{2}{*}{(-1, 1)} & Zero-centered; faster convergence than sigmoid & Hidden layers & Hard to precisely localize deeper brain regions \\ \hline \multirow{4}{*}{Rectified linear unit} & \multirow{4}{*}{\(ReLU(x)=max(0,x)\)} & Simple computation, rapid convergence; can produce ‘dead’ neurons & Hidden layers & Intersubject variability can reduce generalizability \\ \hline \multirow{4}{*}{Softmax} & \multirow{4}{*}{\(softmax(x_{i})=\frac{e^{(x_{i})}}{E\left(e^{(x_{i})}\right)}\)} & \multirow{4}{*}{(0, 1) with sum = 1} & Normalizes outputs into multi-class & Output layer (multi-class) & Requires complex preprocessing (e.g., independent component analysis) to ensure clean signals \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Types and characteristics of activation functions. Building on these previous works, the present study applies a CNN-LSTM hybrid model to EEG emotion analysis for classifying users' emotional preference toward architectural stimuli. Our goal is to achieve a high classification accuracy and robust generalization, while simplifying complex preprocessing steps. \begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **Key Feature** & **Advantage** & **Limitation** \\ \hline Convolutional neural network (CNN) & Spatial pattern recognition & Efficient for spatial electroencephalographic data & Limited temporal pattern learning \\ \hline Long short-term memory (LSTM) & Temporal sequence modeling & Captures long-term dependencies in signals & Computationally expensive \\ \hline CNN-LSTM & Combined spatial-temporal learning & High accuracy and generalization & Complex architecture \\ \hline \end{tabular}
\end{table} 
Table 7: A comparison of key deep learning models. Figure 1: Convolutional neural network long short-term memory structure [98].