Abstract
In recent years, Internet of Things (IoT) technology has brought many applications and developments for wearable devices, and the use of non-invasive electroencephalography (EEG) instruments to measure attention has been a topic of discussion. However, the correlation between attention and cognitive load has rarely been analyzed by data mining. For this reason, this study used head-mounted non-invasive EEG instruments based on IoT technology to collect attention values related to two courses and extracurricular activities and used a cognitive load questionnaire to investigate the cognitive loads of subjects. Correlation analysis was carried out through data mining technology to find the correlation between attention and cognitive load. In addition, six short-term experiments and relaxation experiments were designed to measure the subjects’ maximum attention and minimum attention values, so as to propose a strategy for setting the attention baseline. According to the results of the various experiments, subjects suffering from overload showed a state of inattention during the whole activity while subjects suffering a high load showed low sustained attention; only subjects with a medium load showed high sustained attention. Subjects with a low load showed inattention for nearly the entire activity. In this study, a strategy for setting an attention baseline was proposed to normalize the attention values from different EEG instruments. The correlation between attention value and cognitive load is analyzed using association rule mining technology so that the change of cognitive load could be effectively estimated by measuring the attention value instead of using questionnaire in the future.
Highlights
In an era of booming information, Internet of Things (IoT) technology enables independent subjects to communicate with each other through the Internet
Attention values obtained by wearable EEG instruments were collected, and attention baseline was defined by the experimental design for the maximum attention degree and minimum attention degree
ASSOCIATION RULE MINING This paper studied the association between attention and cognitive load through association of rule data mining
Summary
In an era of booming information, IoT technology enables independent subjects to communicate with each other through the Internet. It has been applied to the issue of Smart Cities for extensive research [1], making IoT one of the important developments in the information technology era [2] and a hot topic of academic research in recent years [3], [4]. IoT technology can be used to develop mobile wearable instruments through the Internet or wireless transmission. Wearable EEG instruments could solve many equipment and environmental limitations and provide many potential benefits when measuring brain wave signals [6]. Some researchers have shown the feasibility of using lowcost wearable EEG to measure brain wave signals [7]. These wearable EEG instruments measure five wavelengths in the human brain with different channel numbers and contact points. Apart from measuring attention through EEG, previous researchers explored the relationship between attention and cognitive load [9]
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