設計以腦波特徵為基之自調適專注力訓練平台

10.6285/MIC.201809_7(2).0019

陳萌智；陸海文

專注力 ； 腦波特徵分類與推論 ； 自調適訓練 ； Attention ； EEG features classifying and inferring ； Adaptive training

管理資訊計算

7卷2期（2018 / 09 / 01）

233 - 257

繁體中文

科技的發達使得現代人同時要處理太多的訊息，在長期一心多用的情況下，整個社會走入分心的黑暗時期。誠然，相關研究已提出學生專注力不足已成為學習的一大警訊，專注力是學習的第一步，在學習過程學生必須主動去注意某事才有學習發生，一旦專注力有了缺陷，其他較複雜的認知功能將會受到影響，故如何發展專注力訓練以提昇未來學習力將是未來教育重點，亦是本研究的主要目標。本研究「以腦波特徵為基自調適數位專注力訓練平台」乃是在數位化訓練過程中透過個人腦波活動偵測推論專注力狀況，即時調整訓練策略以輔導學生進行適性化專注力訓練。故本研究的工作項目包括：專注力訓練領域專家需求訪談，發展專注力訓練案例、專注力腦波特徵分類與推論、自調適數位專注力訓練平台建置。

Developed technology makes modern to deal with too much information at the same time. In the case of long-term multitasking, the whole society fell into the dark age of distraction. Admittedly, related research has proposed that students’ Attention Deficit has become a major warning to learn. Attention is the first step in learning. Students must take the initiative to pay attention to something, then they bechance upon learning in the learning process. Once attention has flaws, other more complex cognitive function will be affected. Therefore how to develop attention training to enhance the learning ability will be both the future focus of education and the major objective of this study. In this plan, the ＂adaptive digital training attention platform based on EEG features＂ will infer the situation of attention through detection of personal brainwave in the digital training process, and then to adjust training strategies real time when student is ongoing attention training adaptively. The content of work in this plan includes: retrieving expert knowledge in the field of attention training, developing training cases and digital design, classifying and inferring EEG-attention feature, constructing adaptive digital training attention platform based on EEG features.

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