Evaluation of rehearsal effects of multimedia content based on EEG using machine learning algorithms

Mazher, M. and Aziz, A.A. and Malik, A.S. (2017) Evaluation of rehearsal effects of multimedia content based on EEG using machine learning algorithms. International Conference on Intelligent and Advanced Systems, ICIAS 2016 .

Full text not available from this repository.

Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....


Rehearsal is a common phenomenon of practicing something to make it more resilient in long-term memory. This paper will present the rehearsal effects based on electroencephalography (EEG) recorded data for multimedia contents. Three frequency based features are used to discriminate the three learning states mentioned as L1, L2 and L3 using machine learning algorithms. From these three learning states, L1 is the first learning state whether L2 and L3 are the rehearsal states of L1. The set of spectral features that are used for analysis are based on the intensity weighted mean frequency (IWMF), its bandwidth (IWBW), and spectral power density (PSD). For the analysis, the three brain waves investigated are the alpha waves, theta waves and delta waves. The results of the study show that the alpha waves produce de-synchronization from rest to learning state as compared to other EEG recorded waves. This de-synchronization lead to mental effort imposed by working memory during a learning task. The Alpha wave shows more accuracy in L1 using SVM classifier that is 85 using PSD features, 86 for IWFM and 78.4 using IBWB feature. The results also mention that L3 produces less classifier accuracy value as compared to the L2 and L1 for each of three extracted features. This indicates that L3 requires less mental effort during learning. The findings proved the rehearsal as a good phenomenon of long-term memorized learning. © 2016 IEEE.

Item Type:Article
Impact Factor:cited By 0
Departments / MOR / COE:Division > Academic > Faculty of Engineering > Electrical & Electronic Engineering
ID Code:20193
Deposited By: Ahmad Suhairi
Deposited On:22 Apr 2018 14:45
Last Modified:22 Apr 2018 14:45

Repository Staff Only: item control page