Features Selection and Pattern Classification of Electroencephalography Motor Imagery Tasks of Right Hand

Ahmed A Ibrahim,Abdulwahab A Alnaqi,Mohammed I Awad,Farid A Tolbah,Ann A Abdel Kader

doi:10.19026/rjaset.14.5129

Abstract

This study presentsa Brain Computer Interface (BCI) approach to detect the motor intents of the disabled people with right hand amputation. Electroencephalography (EEG) Motor Imagery (MI)-based Brain Computer Interface (BCI) systems have been recently used to improve the quality of life of disabled people. However, to naturally trigger particular applications (i.e., upper limb prostheses), independent BCIs appeal further paradigms to involve realistic motor imagery tasks. This study proposes an approach to classifying imagined hand gesture tasks, including the water glass gesture and the index pointer gesture of the right hand using OPENBCI as a consumer-grade EEG acquisition device. For three subjects, the data recorded by OPENBCI were sampled with a sampling rate of 250 Hz. The Minimum Redundancy Maximum Relevance (MRMR) technique was implemented as a feature selection method along with the Support Vector Machine (SVM) algorithm for classification. By obtaining a maximum classification accuracy of 91.7%, the results showed the feasibility of such Brain Computer Interface systems to detect different motor imagery tasks for the right hand. Consequently, upper limb prostheses could be manipulated using the intended motor imagery tasks.

Highlights

In recent decades, diverse neuroscience-related studies have been conducted for attaining a robust perceptron of the human brain activities that reflect the motor imagery tasks of the amputated limb
These results demonstrate the potential of the proposed approach to discriminate between two adjacent Motor Imagery (MI) tasks for the right hand
The proposed approach increased the classification accuracy of the right hand MI tasks compared to PCA method

Summary

Introduction

Diverse neuroscience-related studies have been conducted for attaining a robust perceptron of the human brain activities that reflect the motor imagery tasks of the amputated limb. Among these studies, EEG-based BCI systems are the most widely investigated due to their noninvasiveness and portability (Liu et al, 2012). Non-invasive BCI’s are based on decoding the changes in oscillatory activities caused by motor imagery tasks, exploiting the Event-Related Desynchronization (ERD); a suppression of α and β rhythm power and Event-Related Synchronization (ERS); an increase in α and β rhythm power, phenomena (Pfurtscheller and Lopes de Silva, 1999; Soman et al, 2013). Recent studies have successfully demonstrated the possibility of manipulating prosthetic hands through the classification of the intended MI tasks

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Conclusion