Wavelet-based fractal features with active segment selection: application to single-trial EEG data.

Full Abstract

Feature extraction in brain-computer interface (BCI) work is one of the most important issues that significantly affect the success of brain signal classification. A new electroencephalogram (EEG) analysis system utilizing active segment selection and multiresolution fractal features is designed and tested for single-trial EEG classification. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the proposed system consists of three main procedures including active segment selection, feature extraction, and classification. The active segment selection is based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, and is used to obtain the optimal active time segment in the time-frequency domain. We then utilize a modified fractal dimension to extract multiresolution fractal feature vectors from the discrete wavelet transform (DWT) data for movement classification. By using a simple linear classifier, we find significant improvements in the rate of correct classification over the conventional approaches in all of our single-trial experiments for real finger movement. These results can be extended to see the good adaptability of the proposed method to imaginary movement data acquired from the public databases.

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Author information

Author/s: Hsu, Wei-Yen (WY); Lin, Chou-Ching (CC); Ju, Ming-Shaung (MS); Sun, Yung-Nien (YN);

Affiliation: Department of Computer Science & Information Engineering, Tainan, Taiwan, ROC.

Journal and publication information

Publication Type: Journal Article

Journal: Journal of neuroscience methods (J Neurosci Methods), published in Netherlands. (Language: eng)

Reference: 2007-Jun; vol 163 (issue 1) : pp 145-60

Dates: Created 2007/05/04; Completed 2007/08/15;

PMID: 17379316, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Artificial Intelligence
Brain - physiology
Brain Mapping
Electroencephalography - methods
Fractals

Humans
Neural Networks (Computer)
Signal Processing, Computer-Assisted
Time Factors
User-Computer Interface

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