## 4 Results  
### EEG Sensitivity to Different Fact-Extraction Methods  
#### 4.1.1 Based on Cosine Similarity  
This section reports the inter-class similarities of the EEG vectors corresponding to the factual word groups and non-factual word groups obtained from four factual word extraction methods and the intra-class similarities for each group. The quantities of factual and non-factual words extracted using the four methods (dependency, entity, pos, and TF-IDF) are shown in Table 2.  
Based on the description in Section 3, each 1220 word was recorded with 1.5 s of EEG signal. After averaging across the 28 channels, each word corresponds to a 375-dimensional EEG vector. When computing the cosine similarity of EEG vectors for factual word groups and non-factual word groups obtained from different extraction methods, three distinct time segments of the 1.5-s EEG signal were analyzed for each extraction method. These three segments are as follows: (1) the entire 1.5-s duration, encompassing 375 sampling points, denoted as \"Overall\"; (2) the time window potentially capturing the N400 ERP component, ranging from 250 ms to 500 ms, which includes 62 sampling points, denoted as \"N400\"; and (3) the time window potentially capturing the P600 ERP component, ranging from 500 ms to 1000 ms, containing 125 sampling points, denoted as \"P600\".  
Firstly, the inter-class cosine similarity between the EEG vectors of fact word groups and non-fact word groups obtained from the four extraction methods was computed, with results shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Extraction Methods** & **Number of Fact Words** & **Number of Non-Fact Words** \\ \hline Dependency & 522 & 698 \\ Entity & 250 & 970 \\ Pos & 563 & 657 \\ TF-IDF & 478 & 742 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantities of factual and non-factual words.  
It can be observed that the inter-class cosine similarity between the EEG vectors of fact word groups and non-fact word groups extracted using the Entity and Dependency methods is close to \(-1\). The result indicates that the EEG signals for these fact word groups and non-fact word groups, as identified by these two methods, are highly dissimilar in direction and exhibit clear differentiation. In contrast, the EEG signals' differentiation between the two groups of words extracted based on pos is not pronounced, with the vectors even displaying a degree of positive correlation. Furthermore, the two groups of words extracted using TF-IDF exhibited significant differences in the N400 (often indicative of semantic comprehension) and P600 (typically representing syntactic processing) time windows, suggesting that the TF-IDF method captures some semantic information (as evidenced by the negative similarity in the N400 time window) but overlooks crucial syntactic information (evidenced by the positive similarity in the P600 window).  
Subsequently, the intra-class similarity for factual word groups obtained from the four extraction methods was computed, with the results shown in Table 4.  
All extraction methods yielded a very low intra-class cosine similarity for the factual word group, nearing 0. The result indicates a minimal correlation between the EEG vectors corresponding to the factual words extracted by these four methods. The angles between the vectors are nearly orthogonal, reflecting the brain's highly independent interpretation of each word within the factual word group.  
Lastly, the intra-class similarity for non-factual word groups obtained from the four extraction methods was computed, with the results presented in Table 5.  
All extraction methods resulted in a very low intra-class cosine similarity for the non-factual word group, again approaching 0. The result indicates a minimal correlation between the EEG vectors corresponding to the non-factual words extracted by these four methods, and the brain's comprehension of each word within the non-factual word set is highly independent.