Wheelchair Free Hands Navigation Using Robust DWT_AR Features Extraction Method With Muscle Brain Signals

Zaineb M. Alhakeem,Raed A. Abd-Alhameed,Ramzy S. Ali

doi:10.1109/access.2020.2984538

Zaineb M. Alhakeem, Raed A. Abd-Alhameed + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.2984538

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Abstract

Researchers try to help disabled people by introducing some innovative applications to support and assess their life. The Brain-Computer Interface (BCI) application that covers both hardware and software models, is considered in this work. BCI is implemented based on brain signals to be converted to commands. To increase the number of commands, non-brain source signals are used, such as eye-blinking, teeth clenching, jaw squeezing, and other movements. This paper introduced a low dimensions robust method to detect the eye-blinks and jaw squeezing; so that the method can be applied to drive a wheelchair by using five commands. Our approach is used Discrete Wavelet Transform with Autoregressive to extract the signal’s features. These features are classified by using a linear Support Vector Machine (SVM) classifier. The present method detects every testing sample using a small training set to test and drive a powered wheelchair. The proposed method is fully implemented practically based on binary-coded commands.

Highlights

Brain-Computer Interface (BCI) is a control protocol that converts the brain activity signals to some useful commands to an external device
The recorded signals are classified depending on the biophysical nature of the signal source [1]: Metabolic measured by techniques like (near-infrared spectroscopy (NIRS), Electrophysiological that measured by electroencephalography (EEG), Magnetic signal that measured by magnetoencephalography (MEG) [3] and [4]
As there is only two status, we have proposed a binary code to refer to each command that is used in the BCI system

Summary

Introduction

Brain-Computer Interface (BCI) is a control protocol that converts the brain activity signals to some useful commands to an external device. This system can be classified into two categories: invasive and noninvasive systems, depending on the place of the electrodes that will record the signals from an object. Non-invasive BCI systems use electrodes distributed over specific areas of the scalp [1]. These electrodes are either dry or wet [2]. The recorded signals are classified depending on the biophysical nature of the signal source [1]: Metabolic measured by techniques like (near-infrared spectroscopy (NIRS), Electrophysiological that measured by electroencephalography (EEG), Magnetic signal that measured by magnetoencephalography (MEG) [3] and [4]

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Conclusion