## 3 Materials and Methods
### Metrics Selection and Data Analysis  
#### 3.5.1 Analysis of EEG Sensitivity to Different Fact-Extraction Methods  
In the current study, Python's spaCy library was employed for preprocessing the corpus. As discussed in related works, four extraction methods (dependency, entity, pos, and TF-IDF) were selected. For clarity, any subsequent mention of the term \"word\" in this context refers to a \"token.\"  
For dependency relation extraction (dependency), spaCy initially identified dependency relations among all words. Then it selected labels representing relationships between words, including: 'ROOT', 'nsubj', 'nsubjpass', 'compound', 'poss', 'pcomp', 'comp', 'conj','relcl', 'dobj', 'pobj', 'iobj', 'appos', and 'acl', totaling 14 labels. These labels were considered capable of extracting fact-related information. As for named entity recognition (entity), spaCy tagged proper nouns in the text, such as names of people, places, organizations, and dates. Part-of-speech tagging (pos) was also executed using spaCy, extracting nouns and verbs from the text. When employing the TF-IDF method for extraction, Term Frequency (TF) and Inverse Document Frequency (IDF) were primarily calculated and then multiplied, as presented in the subsequent Formulas (1)-(3). The documents selected for IDF calculation included all entries from the FRANK dataset with word counts below 1000. When calculating TF, all words were considered equally important. However, common words like \"the\" and \"a\" might have appeared frequently but might not have been crucial. Thus,  
Figure 2: EEG channels selected in the study. we needed to downplay the weight of these words. IDF served as a method to reduce the weight of these words. \[\mathit{TF}(t)=\frac{\mathit{Number\ of\ times\ term\ t\ appears\ in\ a\ document}}{\mathit{Total\ number\ of\ terms\ in\ the\ document}} \tag{1}\]  
\[\mathit{IDF}(t)=\log\biggl{(}\frac{\mathit{Total\ number\ of\ documents}}{\mathit{Number\ of\ documents\ with\ term\ in\ it}}\biggr{)} \tag{2}\]  
\[\mathit{TF\_IDF}(t)=\mathit{TF}(t)\times\mathit{IDF}(t) \tag{3}\]  
Cosine similarity can be employed to assess the similarity between vectors. Researchers utilize the cosine similarity of word weight vectors to compare the degree of similarity between documents or the cosine similarity of word vectors to compute the similarity between different words [35]. Inspired by prior studies, this research considers each token's corresponding EEG signal as an EEG vector, using their cosine similarity to assess the sensitivity of EEG signals to different factual word extraction methods. Specifically, after preprocessing, the EEG data from reading the text in this study was saved with a structure of \((n,\ 28,\ 375)\). \(n\) was the number of tokens of the corresponding text after tokenization by spaCy. Here, 28 represented the number of channels for EEG signal collection, and 375 was the number of potentials collected within 1.5 s for each word. After averaging across the 28 channels, the data structure becomes \((n,\ 375)\), suggesting that each token corresponds to a 375-dimensional EEG vector. In addition to the full 1.5 s, this study also established 2 time of interest (TOI) intervals: the 250-500 ms interval, which might contain the N400 ERP component, and the 500-1000 ms interval, which might contain the P600 ERP component. The cosine similarity between the EEG vectors of factual and non-factual word groups is calculated as presented in Equation (4). Here, \(c_{1}\) and \(c_{2}\) represent the centroids of the vectors for the factual and non-factual word groups, respectively. The centroids are determined by taking the mean of all vectors within the factual and non-factual word groups. Unlike the similarity between word vectors, which is always non-negative, the similarity range for EEG vectors lies between \(-1\) and \(1\). A smaller angle between vectors indicates a closer directionality, making the cosine similarity approach 1. Conversely, a larger angle suggests divergent directions, bringing the cosine similarity closer to \(-1\).