EEG-based motor imagery classification using enhanced active segment selection and adaptive classifier

Wei Yen Hsu

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

In this study, an adaptive electroencephalogram (EEG) analysis system is proposed for a two-session, single-trial classification of motor imagery (MI) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the adaptive linear discriminant analysis (LDA) is used for classification of left- and right-hand MI data and for simultaneous and continuous update of its parameters. In addition to the original use of continuous wavelet transform (CWT) and Student's two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the selection of active segments. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. The classification in session 2 is performed by adaptive LDA, which is trial-by-trial updated using the Kalman filter after the trial is classified. Compared with original active segment selection and non-adaptive LDA on six subjects from two data sets, the results indicate that the proposed method is helpful to realize adaptive BCI systems.

Original languageEnglish
Pages (from-to)633-639
Number of pages7
JournalComputers in Biology and Medicine
Volume41
Issue number8
DOIs
Publication statusPublished - Aug 2011
Externally publishedYes

Fingerprint

Imagery (Psychotherapy)
Discriminant Analysis
Discriminant analysis
Electroencephalography
Fractals
Classifiers
Wavelet Analysis
Adaptive systems
Fractal dimension
Evoked Potentials
Kalman filters
Wavelet transforms
Brain
Hand
Statistics
Students

Keywords

  • Active segment selection
  • Adaptive classifier
  • Brain-computer interface (BCI)
  • Electroencephalogram (EEG)
  • Fractal dimension
  • Motor imagery (MI)
  • Wavelet transform

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

Cite this

EEG-based motor imagery classification using enhanced active segment selection and adaptive classifier. / Hsu, Wei Yen.

In: Computers in Biology and Medicine, Vol. 41, No. 8, 08.2011, p. 633-639.

Research output: Contribution to journalArticle

@article{8df2cd86753449a383957b26c20c82df,
title = "EEG-based motor imagery classification using enhanced active segment selection and adaptive classifier",
abstract = "In this study, an adaptive electroencephalogram (EEG) analysis system is proposed for a two-session, single-trial classification of motor imagery (MI) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the adaptive linear discriminant analysis (LDA) is used for classification of left- and right-hand MI data and for simultaneous and continuous update of its parameters. In addition to the original use of continuous wavelet transform (CWT) and Student's two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the selection of active segments. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. The classification in session 2 is performed by adaptive LDA, which is trial-by-trial updated using the Kalman filter after the trial is classified. Compared with original active segment selection and non-adaptive LDA on six subjects from two data sets, the results indicate that the proposed method is helpful to realize adaptive BCI systems.",
keywords = "Active segment selection, Adaptive classifier, Brain-computer interface (BCI), Electroencephalogram (EEG), Fractal dimension, Motor imagery (MI), Wavelet transform",
author = "Hsu, {Wei Yen}",
year = "2011",
month = "8",
doi = "10.1016/j.compbiomed.2011.05.014",
language = "English",
volume = "41",
pages = "633--639",
journal = "Computers in Biology and Medicine",
issn = "0010-4825",
publisher = "Elsevier Limited",
number = "8",

}

TY - JOUR

T1 - EEG-based motor imagery classification using enhanced active segment selection and adaptive classifier

AU - Hsu, Wei Yen

PY - 2011/8

Y1 - 2011/8

N2 - In this study, an adaptive electroencephalogram (EEG) analysis system is proposed for a two-session, single-trial classification of motor imagery (MI) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the adaptive linear discriminant analysis (LDA) is used for classification of left- and right-hand MI data and for simultaneous and continuous update of its parameters. In addition to the original use of continuous wavelet transform (CWT) and Student's two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the selection of active segments. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. The classification in session 2 is performed by adaptive LDA, which is trial-by-trial updated using the Kalman filter after the trial is classified. Compared with original active segment selection and non-adaptive LDA on six subjects from two data sets, the results indicate that the proposed method is helpful to realize adaptive BCI systems.

AB - In this study, an adaptive electroencephalogram (EEG) analysis system is proposed for a two-session, single-trial classification of motor imagery (MI) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the adaptive linear discriminant analysis (LDA) is used for classification of left- and right-hand MI data and for simultaneous and continuous update of its parameters. In addition to the original use of continuous wavelet transform (CWT) and Student's two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the selection of active segments. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. The classification in session 2 is performed by adaptive LDA, which is trial-by-trial updated using the Kalman filter after the trial is classified. Compared with original active segment selection and non-adaptive LDA on six subjects from two data sets, the results indicate that the proposed method is helpful to realize adaptive BCI systems.

KW - Active segment selection

KW - Adaptive classifier

KW - Brain-computer interface (BCI)

KW - Electroencephalogram (EEG)

KW - Fractal dimension

KW - Motor imagery (MI)

KW - Wavelet transform

UR - http://www.scopus.com/inward/record.url?scp=79960632494&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79960632494&partnerID=8YFLogxK

U2 - 10.1016/j.compbiomed.2011.05.014

DO - 10.1016/j.compbiomed.2011.05.014

M3 - Article

VL - 41

SP - 633

EP - 639

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

SN - 0010-4825

IS - 8

ER -