Classification of human concentration in EEG signals using Hilbert Huang transform

Fathrul Azarshah Abdul Aziz, Mohd Ibrahim Shapiai, Noor Akhmad Setiawan, Yasue Mitsukura

Research output: Contribution to journalArticle

Abstract

The electroencephalogram (EEG) is an important technique that allows people to study brain signals. Classifying whether a person is concentrating or not is almost impossible with our naked eye. Thus, a system that could accurately distinguish these two categories is highly valued. Previously, the study to determine a mental state of concentration and non-concentration is based on the Fourier Transform. In this study, we employed Hilbert Huang Transform (HHT) to extract important features for this mental classification task. HHT consists of two step: (1) Using EMD and (2) Hilbert Transform. The EMD will decompose the signal into a collection of Intrinsic Mode Functions (IMF). Then Hilbert Transform is employed to obtain the IMF to compute the power spectrum to extract important features based on different subbands i.e. gamma, beta, alpha, theta and delta. An Extreme Learning Machine (ELM) is then used as a classifier to distinguish the two mental states. Four different sets of investigations were performed using the existing FFT and three investigated the HHT framework including single IMF, two and three IMFs. The results showed that the HHT with single IMF offers slight improvement compared to the existing FFT.

Original languageEnglish
Pages (from-to)10.1-10.11
JournalInternational Journal of Simulation: Systems, Science and Technology
Volume18
Issue number1
DOIs
Publication statusPublished - 2017 Mar 1

Fingerprint

Hilbert-Huang Transform
Intrinsic Mode Function
Electroencephalography
Mathematical transformations
Hilbert Transform
Extreme Learning Machine
Fast Fourier transforms
Power Spectrum
Fourier transform
Person
Classifier
Power spectrum
Decompose
Learning systems
Human
Electroencephalogram
Brain
Fourier transforms
Classifiers

Keywords

  • Electroencephalogram
  • Extreme learning machine
  • Hilbert huang transform

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation

Cite this

Classification of human concentration in EEG signals using Hilbert Huang transform. / Aziz, Fathrul Azarshah Abdul; Shapiai, Mohd Ibrahim; Setiawan, Noor Akhmad; Mitsukura, Yasue.

In: International Journal of Simulation: Systems, Science and Technology, Vol. 18, No. 1, 01.03.2017, p. 10.1-10.11.

Research output: Contribution to journalArticle

Aziz, Fathrul Azarshah Abdul ; Shapiai, Mohd Ibrahim ; Setiawan, Noor Akhmad ; Mitsukura, Yasue. / Classification of human concentration in EEG signals using Hilbert Huang transform. In: International Journal of Simulation: Systems, Science and Technology. 2017 ; Vol. 18, No. 1. pp. 10.1-10.11.
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