# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 4 Application and Usage of Brainwave-Based Fine-Tuned Model in Architectural Spaces
### Fine-Tuned Model Validation and Performance Evaluation
#### 4.1.5 Discussion and Implications
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Version** & **Model** & **Emotion** & **Precision** & **Recall** & **F1 Score** & **Accuracy** & **Cohen’s Kappa** \\ \hline \multirow{3}{*}{Ver. 1} & ft:gpt-4o-2024-08-06-06-personal:eeg- emotion:A59 hbEb & Sad (\(-1\) negative) & 0.5 & 0.04 & 0.07 & \multirow{3}{*}{0.34} & \multirow{3}{*}{0.1} \\  & ft:gpt-4o-2024-08-Neutral (0 neutral) & Neutral (0 neutral) & 0.38 & 0.13 & 0.2 & & \\  & 06:personal:eeg-Positive (1 positive) & Positive (1 positive) & 0.33 & 0.86 & 0.47 & \multirow{3}{*}{0.33} & \multirow{3}{*}{0.1} \\  & & Overall accuracy & - & - & 0.34 & & \\ \hline \multirow{3}{*}{Ver. 2} & ft:gpt-4o-2024-08-06-06-personal:eeg- emotion:A59 hbEb & Sad (\(-1\) negative) & 1.0 & 0.01 & 0.02 & \multirow{3}{*}{0.34} & \multirow{3}{*}{0.1} \\  & 06:personal:eeg- emotion: & Neutral (0 neutral) & 0 & 0 & 0 & & \\  & 06:personal:eeg- emotion: & Positive (1 positive) & 0.32 & 0.99 & 0.49 & & \\  & 06:personal:eeg- emotion: & Overall accuracy & - & - & 0.33 & & \\ \hline \multirow{3}{*}{Ver. 3} & ft:gpt-4o-2024-08-06-06-personal:eeg- emotion:A59 hbEb & Sad (\(-1\) negative) & 0.34 & 0.37 & 0.36 & \multirow{3}{*}{0.34} & \multirow{3}{*}{0.1} \\  & 06:personal:eeg- emotion: & Neutral (0 neutral) & 0.37 & 0.33 & 0.32 & & \\  & 03:AArwL8j & Positive (1 positive) & 0.31 & 0.32 & 0.32 & & \\  & 06:Overall accuracy & - & - & 0.34 & & \\ \hline \multirow{3}{*}{Ver. 4} & ft:gpt-4o-mini-2024-07-08-18-personal:eeg- emotion:A11 mNZ & Sad (\(-1\) negative) & 0.32 & 0.63 & 0.43 & \multirow{3}{*}{0.32} & \multirow{3}{*}{0.1} \\  & 07:A1B1 mNZ & Neutral (0 neutral) & 0.31 & 0.30 & 0.31 & & \\  & 03:A1B1 mNZ & Positive (1 positive) & 0.33 & 0.02 & 0.04 & & \\  & 03:A1B1 mNZ & Overall accuracy & - & - & 0.32 & & \\ \hline \multirow{3}{*}{Ver. 5} & ft:gpt-4o-mini-2024-07-08-18-personal:eeg- emotion:A59 hbEb & Sad (\(-1\) negative) & 0.39 & 0.17 & 0.23 & \multirow{3}{*}{0.38} & \multirow{3}{*}{0.07} \\  & 18:personal:eeg- emotion: & Neutral (0 neutral) & 0.40 & 0.53 & 0.46 & \multirow{3}{*}{0.38} & \multirow{3}{*}{0.07} \\  & 05:A842P4s & Positive (1 positive) & 0.35 & 0.43 & 0.39 & & \\  & 05:A842P4s & Overall accuracy & - & - & 0.38 & & \\ \hline \multirow{3}{*}{Ver. 6} & ft:gpt-4o-mini-2024-07-08-18-personal:eeg- emotion:A59 hbEb & Sad (\(-1\) negative) & 0.32 & 0.52 & 0.40 & \multirow{3}{*}{0.32} & \multirow{3}{*}{0.1} \\  & 08:personal:eeg- emotion: & Neutral (0 neutral) & 0.30 & 0.36 & 0.32 & & \\ \cline{1-1}  & 08:personal:eeg- emotion: & Positive (1 positive) & 0.31 & 0.05 & 0.08 & & \\ \cline{1-1}  & 08:personal:eeg- emotion: & Overall accuracy & - & - & 0.31 & & \\ \hline \multirow{3}{*}{Ver. 7} & ft:gpt-4o-mini-2024-07-08-18-personal:eeg- emotion:A59 hbEb & Sad (\(-1\) negative) & 0.35 & 0.44 & 0.39 & \multirow{3}{*}{0.32} & \multirow{3}{*}{0.1} \\  & 08:personal:eeg- emotion: & Neutral (0 neutral) & 0.31 & 0.39 & 0.35 & & \\ \cline{1-1}  & 08:personal:eeg- emotion: & Positive (1 positive) & 0.28 & 0.13 & 0.18 & & \\ \cline{1-1}  & 08:personal:eeg- emotion: & Overall accuracy & - & - & 0.32 & & \\ \hline \multirow{3}{*}{Ver. 8} & ft:gpt-4o-mini-2024-07-08-18-personal:eeg- emotion:A59 hbEb & Sad (\(-1\) negative) & 0.6 & 0.52 & 0.56 & \multirow{3}{*}{0.47} & \multirow{3}{*}{0.51} \\  & 08:personal:eeg- emotion: & Neutral (0 neutral) & 0.55 & 0.47 & 0.51 & & \\ \cline{1-1}  & 08:personal:eeg- emotion: & Positive (1 positive) & 0.62 & 0.81 & 0.70 & & \\ \cline{1-1}  & 08:personal:eeg- emotion: & Overall accuracy & - & - & 0.603 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Version 8 dataset structure. Additionally, the study investigated the impact of training LLM models using JSONL files without EEG channel names, enabling the model to process EEG signals without explicit spatial references. This method not only enhances data privacy but also encourages the model to focus on intrinsic signal patterns rather than surface-level spatial identifiers.