EEG-based brain-computer interface methods with the aim of rehabilitating advanced stage ALS patients.
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 communi...
| Published in: | Disability & Rehabilitation: Assistive Technology Vol. 19; no. 8; pp. 3183 - 3194 |
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| Main Authors: | , , |
| Format: | pictorial review tables/charts Journal Article |
| Published: |
Taylor & Francis Ltd
Nov2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180828086&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180828086 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17483107 1X04 jtl: Disability & Rehabilitation: Assistive Technology issn: 17483107 maglogo: Y pubinfo: dt: Nov2024 vid: 19 iid: 8 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 180828086 175626512 180828086 180828086 10.1080/17483107.2024.2316312 180828086 ppf: 3183 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: EEG-based brain-computer interface methods with the aim of rehabilitating advanced stage ALS patients. aug: au: Pirasteh, Alireza Shamseini Ghiyasvand, Manouchehr Pouladian, Majid affil: Department of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran sug: subj: Amyotrophic Lateral Sclerosis Rehabilitation Electroencephalography Methods Neural Networks (Computer) User-Computer Interface Dimensionality Reduction Support Vector Machine Communication Deep Learning Signal Processing, Computer Assisted ab: 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. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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