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Cognitive task difficulty analysis using EEG and data mining

Duraisingam, A., Palaniappan, Ramaswamy, Andrews, S. (2017) Cognitive task difficulty analysis using EEG and data mining. In: Proceedings of the 2017 Conference on Emerging Devices and Smart Systems (ICEDSS). . pp. 52-57. IEEE ISBN 978-1-5090-5555-5. (doi:10.1109/ICEDSS.2017.8073658) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/ICEDSS.2017.8073658

Abstract

Existing research on task difficulty and program comprehension mainly concentrate on brain areas related to attention and meditation. In this research, an in-depth analysis of Task Difficulty Level (TDL) for program comprehension is proposed with features extracted from different areas of the brain. Two levels of task difficulty were analysed: easy and difficult. Eight students were asked to solve nine Java programs of different difficulty level and the subject's cognitive load was recorded using EEG. Four different feature extraction methods were used for analysis - Energy, Frequency ratio, Event Related De-Synchronization (ERD) and Asymmetry ratio and Naïve Bayes classifier was used for classifying different TDL. The results indicated that the recorded EEG signals could reflect TDL for program comprehension tasks for predicting easy and difficult tasks. The classifier predicted task difficulty (easy/difficult) of a new task with an overall correctly classified accuracy of 76.55% with a precision of 80.03% and recall of 76.66%. This in-depth analysis of TDL for program comprehension tasks using EEG could support in developing future generation learning tools.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/ICEDSS.2017.8073658
Additional information: Unmapped bibliographic data: C7 - 8073658 [EPrints field already has value set] LA - English [Field not mapped to EPrints] J2 - Conf. Emerg. Devices Smart Syst., ICEDSS [Field not mapped to EPrints] AD - Data Science (E-Health) Research Group, School of Computing, University of Kent, Chatham, United Kingdom [Field not mapped to EPrints] AD - Mahendra Engineering College, Salem, India [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Conference Paper [Field not mapped to EPrints] A4 - [Field not mapped to EPrints] C3 - 2017 Conference on Emerging Devices and Smart Systems, ICEDSS 2017 [Field not mapped to EPrints]
Uncontrolled keywords: Electroencephalogram, Naive Bayes classifier, Program comprehension, Task difficulty, Classifiers, Computer programming, Data mining, Digital to analog conversion, Education, Electroencephalography, Desynchronization, Feature extraction methods, Frequency ratios, Future generations, In-depth analysis, Naive Bayes classifiers, Program comprehension, Task difficulty, Computer software
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 12:18 UTC
Last Modified: 30 May 2019 08:28 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70673 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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