Classification of brainwave using data mining in producing an emotional model

Nurshuhada Mahfuz, Waidah Ismail, Zalisham Jali, Khairul Anuar, Md. Jan Nordin

Research output: Contribution to journalArticle

Abstract

In this paper, classification of brainwave using real world data from Parkinson’s patients is presented. Emotional model is produced from the classification of brainwave. Electroencephalograph (EEG) signal is recorded on eleven Parkinson’s patients. This paper aim to find the “best” classification for the emotional model in brainwave patterns for the Parkinson’s disease. The work performed based on the two method phases which are using the raw data and pre- processing data. In each of the method, we performed for steps in the sum of the hertz and divided by total hertz. In the pre-processing data we are using statistic mean and standard deviation. We used WEKA Application for the classification with 11 fold validation. As a results, implecart from the classification tree performed the “best” classification for the emotional model for Parkinson Patients. The Simplecart classification result is 84.42% accuracy.

Original languageEnglish
Pages (from-to)128-136
Number of pages9
JournalJournal of Theoretical and Applied Information Technology
Volume75
Issue number2
Publication statusPublished - 2015

Fingerprint

Data mining
Data Mining
Model
Classification Tree
Parkinson's Disease
Data Preprocessing
Mean deviation
Standard deviation
Statistic
Preprocessing
Emotion
Fold
Statistics

Keywords

  • Brainwave
  • Classification
  • Emotional model
  • Parkinson patients

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Classification of brainwave using data mining in producing an emotional model. / Mahfuz, Nurshuhada; Ismail, Waidah; Jali, Zalisham; Anuar, Khairul; Nordin, Md. Jan.

In: Journal of Theoretical and Applied Information Technology, Vol. 75, No. 2, 2015, p. 128-136.

Research output: Contribution to journalArticle

Mahfuz, Nurshuhada ; Ismail, Waidah ; Jali, Zalisham ; Anuar, Khairul ; Nordin, Md. Jan. / Classification of brainwave using data mining in producing an emotional model. In: Journal of Theoretical and Applied Information Technology. 2015 ; Vol. 75, No. 2. pp. 128-136.
@article{b2df16eb45ab439da25827994908aa2a,
title = "Classification of brainwave using data mining in producing an emotional model",
abstract = "In this paper, classification of brainwave using real world data from Parkinson’s patients is presented. Emotional model is produced from the classification of brainwave. Electroencephalograph (EEG) signal is recorded on eleven Parkinson’s patients. This paper aim to find the “best” classification for the emotional model in brainwave patterns for the Parkinson’s disease. The work performed based on the two method phases which are using the raw data and pre- processing data. In each of the method, we performed for steps in the sum of the hertz and divided by total hertz. In the pre-processing data we are using statistic mean and standard deviation. We used WEKA Application for the classification with 11 fold validation. As a results, implecart from the classification tree performed the “best” classification for the emotional model for Parkinson Patients. The Simplecart classification result is 84.42{\%} accuracy.",
keywords = "Brainwave, Classification, Emotional model, Parkinson patients",
author = "Nurshuhada Mahfuz and Waidah Ismail and Zalisham Jali and Khairul Anuar and Nordin, {Md. Jan}",
year = "2015",
language = "English",
volume = "75",
pages = "128--136",
journal = "Journal of Theoretical and Applied Information Technology",
issn = "1992-8645",
publisher = "Asian Research Publishing Network (ARPN)",
number = "2",

}

TY - JOUR

T1 - Classification of brainwave using data mining in producing an emotional model

AU - Mahfuz, Nurshuhada

AU - Ismail, Waidah

AU - Jali, Zalisham

AU - Anuar, Khairul

AU - Nordin, Md. Jan

PY - 2015

Y1 - 2015

N2 - In this paper, classification of brainwave using real world data from Parkinson’s patients is presented. Emotional model is produced from the classification of brainwave. Electroencephalograph (EEG) signal is recorded on eleven Parkinson’s patients. This paper aim to find the “best” classification for the emotional model in brainwave patterns for the Parkinson’s disease. The work performed based on the two method phases which are using the raw data and pre- processing data. In each of the method, we performed for steps in the sum of the hertz and divided by total hertz. In the pre-processing data we are using statistic mean and standard deviation. We used WEKA Application for the classification with 11 fold validation. As a results, implecart from the classification tree performed the “best” classification for the emotional model for Parkinson Patients. The Simplecart classification result is 84.42% accuracy.

AB - In this paper, classification of brainwave using real world data from Parkinson’s patients is presented. Emotional model is produced from the classification of brainwave. Electroencephalograph (EEG) signal is recorded on eleven Parkinson’s patients. This paper aim to find the “best” classification for the emotional model in brainwave patterns for the Parkinson’s disease. The work performed based on the two method phases which are using the raw data and pre- processing data. In each of the method, we performed for steps in the sum of the hertz and divided by total hertz. In the pre-processing data we are using statistic mean and standard deviation. We used WEKA Application for the classification with 11 fold validation. As a results, implecart from the classification tree performed the “best” classification for the emotional model for Parkinson Patients. The Simplecart classification result is 84.42% accuracy.

KW - Brainwave

KW - Classification

KW - Emotional model

KW - Parkinson patients

UR - http://www.scopus.com/inward/record.url?scp=84929414103&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84929414103&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84929414103

VL - 75

SP - 128

EP - 136

JO - Journal of Theoretical and Applied Information Technology

JF - Journal of Theoretical and Applied Information Technology

SN - 1992-8645

IS - 2

ER -