A4 Conference proceedings

Optimized Mother Wavelet in a Combination of Wavelet Packet with Detrended Fluctuation Analysis for Controlling a Remote Vehicle with Imagery Movement: A Brain Computer Interface Study

Publication Details
Authors: Hekmatmanesh Amin, Wu Huapeng, Li Ming, Nasrabadi Ali Motie, Handroos Heikki
Editors of book: Giuseppe, Carbone; Marco, Ceccarelli; Doina, Pisla
Edition name or number: 1
Publication year: 2018
Language: English
Related Journal or Series Information: Mechanisms and Machine Science
Title of parent publication: New Trends in Medical and Service Robotics
Volume number: 65
ISBN: 978-3-030-00328-9
eISBN: 978-3-030-00329-6
ISSN: 2211-0984
JUFO-Level of this publication: 1
Open Access: Not an Open Access publication


Brain computer
interface (BCI) is a critical field in health care to help paralyzed or maim
patients back to normal life. This study is focusing on feature extraction
based on self-similarity concept in electroencephalography (EEG) signal
processing. To this purpose, a combination of discrete Wavelet Packet Transform
(WPT) with Detrended Fluctuation Analysis (DFA) is utilized. Also, Event
Related Desynchronization (ERD) patterns are used for customizing mother
wavelets in the wavelet processing. Therefore, right hand movement imagination
ERDs are extracted and used as a mother wavelet in the WPT algorithm and
updated automatically for individual subjects. The combination of Optimized WPT
with DFA (OWPT-DFA) is utilized for feature extraction for the two classes of
right hand imagination and no-imagination. The features are classified and a
model is trained for online processing by Soft Margin Support Vector Machine
classifier and Generalized Radial basis Function (SSVM-GRBF) kernel. The model
is employed to control a remote vehicle for two state of move forward and stop.
In the experiment, nine subjects are participated to record data and control
the remote vehicle. Results depicted that the OWPT-DFA method’s accuracy reach
to 85.33% with p<0.001 and 75.23% with p<0.05 for offline and online
processing, respectively. It is concluded that the self-similarity concept in
the combination of OWPT and DFA methods with SSVM-GRBF classifier improve the
results of movement imagination detection significantly.


Last updated on 2019-13-03 at 12:00

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