在現代學習環境中，專注力的維持成為學生面臨的一大挑戰，尋找有效的方法來提升專注力，將有助於學生獲得更優秀的學習成果。而過去的研究表明，音樂，尤其是受試者喜愛的音樂，對提升專注力具有積極作用。然而，傳統的專注力評估方法大多缺乏足夠的客觀性和精確性。本研究以多模態實驗的方式，利用心理運動警覺性任務（PVT）和注意力網絡測試（ANT）這兩項行為測試，並結合音樂的干預，探討音樂對專注力的提升效果。PVT反應時間及ANT警覺效應分數結果皆發現音樂可以顯著提升專注程度（p=0.013/0.037）。在機器學習的算法當中，使用Fisher Score特徵選擇的演算法以進行專注力分析。在驗證機器學習的演算法時，使用一個開源的專注腦波資料庫做為訓練集、自採集數據做為測試集的準確率可以達到0.96。本研究不僅驗證音樂對專注力的提升效果，還將為教育領域及腦機介面的應用提供理論支持，特別是在專注力提升與學習效率的相關研究中。

In modern learning environments, maintaining attention has become a significant challenge for students. Finding effective methods to enhance concentration can greatly contribute to improved academic performance. Previous studies have shown that music—especially music preferred by the listener—can have a positive effect on attention enhancement. However, traditional methods for assessing attention often lack sufficient objectivity and precision.
This study adopts a multimodal experimental approach, combining two behavioral tasks—the Psychomotor Vigilance Task (PVT) and the Attention Network Test (ANT)—with music intervention to investigate the impact of music on attention. The results of PVT reaction time and ANT alerting effect scores indicate that music can significantly improve attentional performance (p = 0.013 / 0.037).
For attention analysis, this study employs machine learning algorithms using Fisher Score-based feature selection. In the evaluation of these algorithms, a public EEG dataset related to attention was used as the training set, while self-collected data served as the testing set, achieving an accuracy of 0.96.
This research not only validates the positive effect of music on attention but also provides theoretical support for applications in the fields of education and brain–computer interface (BCI), particularly in studies related to attention enhancement and learning efficiency.

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