DocumentCode :
3661940
Title :
Brain-controlled wheelchair based EEG-SSVEP signals classified by nonlinear adaptive filter
Author :
Arjon Turnip;M. Agung Suhendra; Mada Sanjaya W. S.
Author_Institution :
Technical Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences, Bandung, Indonesia
fYear :
2015
Firstpage :
905
Lastpage :
908
Abstract :
In this paper, an extraction for brain-controlled wheelchair by applying nonlinear adaptive filter on EEG-SSVEP is proposed. A four-choice signal paradigm with differents frequencies (i.e., from 6 to 9 Hz for left, right, bottom, and top, respectively) is used to stimulate the four subjects (about 25±1 years old) in the experiment. The experimental results show that the application of the extraction method achieves a very significant statistical improvement in extracting peak amplitude features.
Keywords :
"Feature extraction","Wheelchairs","Biological neural networks","Electroencephalography","Adaptive filters","Visualization","Noise"
Publisher :
ieee
Conference_Titel :
Rehabilitation Robotics (ICORR), 2015 IEEE International Conference on
ISSN :
1945-7898
Electronic_ISBN :
1945-7901
Type :
conf
DOI :
10.1109/ICORR.2015.7281318
Filename :
7281318
Link To Document :
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