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Abstract
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that leads to progressive muscle weakness and paralysis, ultimately resulting in the loss of ability to communicate and control the environment. EEG-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control with the aim of rehabilitating ALS patients. In particular, P300-based BCI has been widely studied and used for ALS rehabilitation. Other EEG-based BCI methods, such as Motor Imagery (MI) based BCI and Hybrid BCI, have also shown promise in ALS rehabilitation. Nonetheless, EEG-based BCI methods hold great potential for improvement. This review article introduces and reviews FFT, WPD, CSP, CSSP, CSP, and GC feature extraction methods. The Common Spatial Pattern (CSP) is an efficient and common technique for extracting data properties used in BCI systems. In addition, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Networks (NN), and Deep Learning (DL) classification methods were introduced and reviewed. SVM is the most appropriate classifier due to its insensitivity to the curse of dimensionality. Also, DL is used in the design of BCI systems and is a good choice for BCI systems based on motor imagery with big datasets. Despite the progress made in the field, there are still challenges to overcome, such as improving the accuracy and reliability of EEG signal detection and developing more intuitive and user-friendly interfaces By using BCI, disabled patients can communicate with their caregivers and control their environment using various devices, including wheelchairs, and robotic arms.
IMPLICATIONS FOR REHABILITATION
Electroencephalography (EEG)-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control for Amyotrophic Lateral Sclerosis (ALS) patients.
EEG constitutes the most significant input in BCIs and can be successfully used in the neuro-rehabilitation of patients with stroke symptoms and amyotrophic lateral sclerosis.
EEG based BCIs have the potential to provide a means of communication and control for individuals with severe disabilities.a variety of EEG-based BCI methods have been developed with the aim of rehabilitating disabled patients.
Acknowledgements
This article was extracted from the thesis prepared by Alireza Pirasteh. The authors acknowledge the Department of Biomedical Engineering of South Tehran Branch Azad University, and the Research Deputy for the support of the research.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Correction Statement
This article has been corrected with minor changes. These changes do not impact the academic content of the article.