EEG-Based Detection Model for Evaluating and Improving Learning Attention

被引:24
作者
Chiang, Hsiu-Sen [1 ]
Hsiao, Kuo-Lun [1 ]
Liu, Liang-Chi [1 ]
机构
[1] Natl Taichung Univ Sci & Technol, Dept Informat Management, 129 Sanmin Rd,Sec 3, Taichung 40401, Taiwan
关键词
Electroencephalography (EEG); Attention; Attention recovery; Learning performance; Associative Petri net; SUSTAINED ATTENTION; PERFORMANCE; THETA; DYNAMICS; FILTER; ALPHA;
D O I
10.1007/s40846-017-0344-z
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
People are often tempted by external factors which lead to decreased attention. Failing to concentrate over the long term will gradually cause the inability to focus, which creates obstacles for learning and decreases one's ability to learn. However, using the proper recovery method, attention can be restored, thereby improving learning effectiveness. Thus, how to measure a student's attention level precisely, and how to provide an effective attention recovery method for are topics worth attention in the field of learning. An attention level assessment model based on EEG analysis is developed to measure subjects' attention level precisely during learning exercises. The study also observes the relationship between brain wave changes and varying attention levels during learning, and provides attention recovery methods that can help students restore attention and improve their learning efficiency. The study finds that napping is a good recovery method which can help male and female leaners recover their focus states. Conversely, adopting a recovery method which the participant finds more attractive (e.g., playing mobile games or watching YouTube) leads to increased focus on the more attractive activity, and fails to restore attention to the original task.
引用
收藏
页码:847 / 856
页数:10
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