Higher Order Statistics and Phase Synchronization as Features in a Motor Imagery Paradigm

Oana-Diana Hrisca-Eva,Anca Mihela Lazar,Madalina-Giorgiana Murariu

doi:10.14569/ijacsa.2021.0120815

Oana-Diana Hrisca-Eva, Anca Mihela Lazar + Show 1 more

Open Access

https://doi.org/10.14569/ijacsa.2021.0120815

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Abstract

The paper proposes an approach based on higher order statistics and phase synchronization for detection and classification of relevant features in electroencephalographic (EEG) signals recorded during the subjects are performing motor tasks. The method was tested on two different datasets and the performance was evaluated using k nearest neighbor classifier. The results (classification rates higher than 90%) have shown that the method can be used for discriminating right and left motor imagery tasks as an offline analysis for EEG in a brain computer interface system.

Highlights

The brain computer interface (BCI) system has aroused a real interest, as it has become an important tool in translating the measured brain activity into control commands.The BCI was developed for biomedical applications, which led to the development of assistive devices for restoring movement and communication force for patients with disabilities
The aim of the paper is to propose a combination of features which includes higher-order statistics (HOS) based on bispectrum and bicoherence and phase synchronization based on phase locking value and phase lag index
The bicoherence of an EEG signal corresponding to right hand motor imagery for channel C3, respectively the bicoherence of an EEG signal corresponding to left hand motor imagery for channel C4 are shown in Fig. 3 and Fig. 4

Summary

Introduction

The brain computer interface (BCI) system has aroused a real interest, as it has become an important tool in translating the measured brain activity into control commands. The BCI was developed for biomedical applications, which led to the development of assistive devices for restoring movement and communication force for patients with disabilities. The use of electroencephalography (EEG) in the state-of-the-art of brain-computer interface technology has expanded to enhance quality of life, with medical and nonmedical applications [1]. The electroencephalogram is a source of information often used in BCI because it records the electrical activity of the brain with the help of the attached electrodes on the scalp. BCI allows patients with paralysis or motor disorders to have an alternative method of communication and control

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