%0 Journal Article
%@ 21693536
%A Mazher, M.
%A Abd Aziz, A.
%A Malik, A.S.
%A Ullah Amin, H.
%D 2017
%F scholars:8500
%I Institute of Electrical and Electronics Engineers Inc.
%J IEEE Access
%K Brain; E-learning; Electroencephalography; Electrophysiology; Extraction; Feature extraction; Frequency bands, Cognitive loads; Effective connectivities; Learning phase; Learning process; Learning tasks; Multi-media learning; Partial directed coherence; Partial directed coherences (PDC), Biomedical signal processing
%P 14819-14829
%R 10.1109/ACCESS.2017.2731784
%T An EEG-Based Cognitive Load Assessment in Multimedia Learning Using Feature Extraction and Partial Directed Coherence
%U https://khub.utp.edu.my/scholars/8500/
%V 5
%X Assessing cognitive load during a learning phase is important, as it assists to understand the complexity of the learning task. It can help in balancing the cognitive load of postlearning and during the actual task. Here, we used electroencephalography (EEG) to assess cognitive load in multimedia learning task. EEG data were collected from 34 human participants at baseline and a multimedia learning state. The analysis was based on feature extraction and partial directed coherence (PDC). Results revealed that the EEG frequency bands and activated brain regions that contribute to cognitive load differed depending on the learning state. We concluded that cognitive load during multimedia learning can be assessed using feature extraction and measures of effective connectivity (PDC). © 2013 IEEE.
%Z cited By 51