腦波圖在網路搜尋研究中的應用：針對樣本大小、活躍作者和研究結果的系統性回顧

The Application of EEG in Web Search Research: A Systematic Review on Sample Size, Active Authors and Results

儘管腦波圖（EEG）在網路搜尋研究中的應用日增，但樣本量少、研究方法龐雜且普遍性有限等問題依然存在。本研究採用系統性回顧，遵循PRISMA框架，探討EEG在網路搜尋研究的應用，在多個學術資料庫搜索並篩選相關文獻後，針對樣本大小、研究方法和遭遇的挑戰進行分析。結果顯示，EEG研究的樣本量通常在10到24人之間，主要受限於資源。研究者面臨的挑戰包括生物假象、情緒和認知狀態的複雜性，以及小樣本量導致的代表性不足，而設備品質和方法一致性等問題也增加了研究難度。整體而言，EEG在網路搜尋研究中具有龐大潛力，但仍需解決上述挑戰，以提升研究結果的穩健性和適用性；本研究建議未來應著重於改進研究方法、探索創新技術，克服現有侷限。

Introduction. Notwithstanding the growing interest in employing electroencephalography (EEG) for web search research, studies presently being conducted continue to grapple with significant issues, namely, small sample sizes, variability in methodology, and limited generalisability. This systematic review seeks to address these particular issues by describing how EEG has been used in this field of research, addressing sample sizes, active authors, methodologies, and limitations.
Method. This systematic review employs the PRISMA framework to analyse the application of EEG in web search studies, focusing on sample sizes, methodologies, and challenges faced by researchers. A comprehensive search was conducted across multiple academic databases to identify relevant studies.
Results. Findings indicate that typical sample sizes in EEG studies range from 10 to 24 participants, largely due to resource constraints. Researchers encounter challenges such as biological artefacts affecting data quality, the complexity of emotional and cognitive states, and limitations in generalisability due to small sample sizes. Additionally, issues related to equipment quality and methodological consistency further complicate EEG research in this domain.
Conclusions. The application of EEG in web search research holds significant potential for enhancing our understanding of user interactions with search engines. However, addressing the identified challenges is crucial for improving the robustness and applicability of findings. Future studies should focus on refining methodologies and exploring innovative approaches to overcome existing limitations in EEG research related to web searches.