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Classifying Three Imaginary States of the Same Upper Extremity Using Time-Domain Features

Resource type
Date created
2017-03-30
Authors/Contributors
Author: Yong, Xinyi
Author: Menon, Carlo
Abstract
Brain-computer interface (BCI) allows collaboration between humans and machines. It translates the electrical activity of the brain to understandable commands to operate a machine or a device. In this study, we propose a method to improve the accuracy of a 3-class BCI using electroencephalographic (EEG) signals. This BCI discriminates rest against imaginary grasps and elbow movements of the same limb. This classification task is challenging because imaginary movements within the same limb have close spatial representations on the motor cortex area. The proposed method extracts time-domain features and classifies them using a support vector machine (SVM) with a radial basis kernel function (RBF). An average accuracy of 74.2% was obtained when using the proposed method on a dataset collected, prior to this study, from 12 healthy individuals. This accuracy was higher than that obtained when other widely used methods, such as common spatial patterns (CSP), filter bank CSP (FBCSP), and band power methods, were used on the same dataset. These results are encouraging and the proposed method could potentially be used in future applications including BCI-driven robotic devices, such as a portable exoskeleton for the arm, to assist individuals with impaired upper extremity functions in performing daily tasks.
Document
Published as
Tavakolan M, Frehlick Z, Yong X, Menon C (2017) Classifying three imaginary states of the same upper extremity using time-domain features. PLoS ONE 12(3): e0174161. DOI: 10.1371/journal.pone.0174161.
Publication title
PLoS ONE
Document title
Classifying three imaginary states of the same upper extremity using time-domain features
Date
2017
Volume
12
Issue
3
Publisher DOI
10.1371/journal.pone.0174161
Copyright statement
Copyright is held by the author(s).
Scholarly level
Peer reviewed?
Yes
Language
English
Member of collection
Download file Size
journal.pone_.0174161.pdf 2.71 MB

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