## 4 Results
### EEG Sensitivity to Different Fact-Extraction Methods
#### 4.1.2 Based on EEG Signals  
In order to visually illustrate the disparities in brain activity between factual and non-factual words acquired through four extraction methods (dependency, entity, pos, TF-IDF), corresponding EEG signal curves were graphed, as depicted in Figures 4-7. Figure 4  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**TOI** & **Dependency** & **Entity** & **Pos** & **TF-IDF** \\ \hline Overall & 0.0023 & \(-\)0.0007 & 0.0018 & 0.0150 \\ N400 & 0.0038 & \(-\)0.0022 & 0.0024 & 0.0175 \\ P600 & 0.0023 & \(-\)0.0060 & 0.0021 & 0.0144 \\ \hline \hline \end{tabular} Overall: 0–1500 ms; N400: 250–500 ms; P600: 500–1000 ms.  
\end{table}
Table 4: The intra-class similarity of the EEG vectors of the factual word group.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**TOI** & **Dependency** & **Entity** & **Pos** & **TF-IDF** \\ \hline Overall & \(-\)0.90 & \(-\)0.99 & 0.07 & \(-\)0.52 \\ N400 & \(-\)0.94 & \(-\)1.0 & 0.88 & \(-\)0.45 \\ P600 & \(-\)0.94 & \(-\)1.0 & 0.89 & 0.44 \\ \hline \hline \end{tabular} Overall: 0–1500 ms; N400: 250–500 ms; P600: 500–1000 ms.  
\end{table}
Table 3: The inter-class similarity between the EEG vectors of factual and non-factual word groups.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**TOI** & **Dependency** & **Entity** & **Pos** & **TF-IDF** \\ \hline Overall & 0.0094 & 0.0140 & 0.0106 & 0.0021 \\ N400 & 0.0087 & 0.0140 & 0.0109 & 0.0020 \\ P600 & 0.0107 & 0.0156 & 0.0120 & 0.0031 \\ \hline \hline \end{tabular} Overall: 0–1500 ms; N400: 250–500 ms; P600: 500–1000 ms.  
\end{table}
Table 5: The intra-class similarity for the EEG vectors of the non-factual word group.  
corresponds to the four extraction techniques. The curves represent the average EEG signals over 1.5 s for 14 participants reading all factual or non-factual words. The semi-transparent regions on either side of the curves indicate the voltage's standard error (SE) at corresponding time points. In the legend, \"N\" represents the number of EEG signals used for computing the average. For instance, in Figure 4, 7308 indicates that 14 participants read 522 factual words. For each extraction method, 4 subfigures display the voltage from different channels (Area of Interest, AOI). From left to right, these are all 28 channels (Overall), 10 channels located above the frontal lobe (Frontal), 10 channels above the central region (Central), and 8 channels above the parietal lobe (Parietal).  
Observation of the EEG signal curves reveals that it might be challenging to discern a clear distinction between the fact and non-factual word sets derived using the \"Dependency\", \"Pos\", and \"TF-IDF\" methods, as their voltage curves do not show significant differences. However, a marked contrast is observed with the curves obtained using the \"Entity\" method. In all four AOIs the EEG signal curves for factual and non-factual word sets identified through the \"entity\" method never intersect. This distinction is even more pronounced in the Overall, Frontal, and Central AOIs, where the signal curves, including their semi-transparent standard error margins, remain separate. This outcome aligns with previous findings based on cosine similarity. Additionally, research has shown that human brains exhibit discernible differences in electro-cortical manifestations when processing common and proper nouns during reading tasks [39]. Proper nouns are often extracted as factual words using the Entity method. These results further underscore that the distinction between groups of factual and non-factual words extracted through named entity recognition (entity) is most pronounced in the EEG signals.