Classifying three imaginary states of the same upper extremity using time-domain features.

Mojgan Tavakolan,Xinyi Yong,Zack Frehlick,Carlo Menon,Bin He

doi:10.1371/journal.pone.0174161

Mojgan Tavakolan, Xinyi Yong + Show 3 more

Open Access

https://doi.org/10.1371/journal.pone.0174161

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Journal: PloS one	Publication Date: Mar 30, 2017
Citations: 34	License type: CC BY 4.0

Affiliation: Simon Fraser University

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.

Highlights

Brain-computer interfaces (BCIs) are a promising tool for detecting user intention and controlling robotic devices [1, 2]
The observed topographical differences were participant-specific, so that no consistent patterns could be observed between participants, which is consistent with previous study (PS) [15]
We proposed a scheme for processing EEG signals and discriminating among three different motor imagery tasks all involving the same limb

Summary

Introduction

Brain-computer interfaces (BCIs) are a promising tool for detecting user intention and controlling robotic devices [1, 2]. While both invasive and non-invasive methods have been proposed for acquiring brain signals [3, 4], this study focuses on a non-invasive BCI based on EEG. BCIs rely on signal processing to identify changes in brain activity corresponding to different mental tasks. Various combinations of extracted features and classification algorithms, with different degrees of complexity and efficiency, have been proposed in the literature for EEG signal processing [9, 10].

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Conclusion