Enhancing Classification Accuracy of fNIRS-BCI for Gait Rehabilitation-Reference-Cited by-同舟云学术

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Enhancing Classification Accuracy of fNIRS-BCI for Gait Rehabilitation

Published:2024 Issue: Volume:12 Page:117944-117954
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Shabbir Minhas Hamza¹^ORCID,Nazeer Hammad¹^ORCID,Naseer Noman¹,Shahbaz Khan Umar²^ORCID,Ansari Ali R.³^ORCID,Nawaz Raheel⁴^ORCID

Affiliation:

1. Department of Mechatronics and Biomedical Engineering, Air University, Islamabad, Pakistan

2. Department of Mechatronics Engineering, National University of Sciences and Technology, Islamabad, Pakistan

3. Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Mishref Campus, Hawally, Kuwait

4. VC Office, Staffordshire University, Stoke-on-Trent, U.K.

Funder

Gulf University for Science and Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10634460.pdf?arnumber=10634460

Reference65 articles.

1. Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

2. Increased prefrontal cortical activation during challenging walking conditions in persons with lower limb amputation – an fNIRS observational study

3. Identification of gait imagery based on fNIRS and class-dependent sparse representation

4. Embodiment for Robotic Lower-Limb Exoskeletons: A Narrative Review

5. Rehabilitation after Stroke

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