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

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...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.64266-64277
Main Authors: Alhakeem, Zaineb M., Ali, Ramzy S., Abd-Alhameed, Raed A.
Format: Article
Language:English
Subjects:
Autoregressive
Autoregressive processes
Blinking
brain-computer interface
coded commands
Commands
Compressing
Discrete Wavelet Transform
driving a wheelchair
electroencephalography
Feature extraction
Human-computer interface
People with disabilities
Robustness
Support vector machines
Wavelet transforms
Wheelchairs
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2984538