{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T16:18:14Z","timestamp":1782577094326,"version":"3.54.5"},"reference-count":64,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T00:00:00Z","timestamp":1681171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University Kebangsaan Malaysia and Ministry of Education, Malaysia","award":["FRGS\/1\/2021\/TK0\/UKM\/01\/4"],"award-info":[{"award-number":["FRGS\/1\/2021\/TK0\/UKM\/01\/4"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The second leading cause of death and one of the most common causes of disability in the world is stroke. Researchers have found that brain\u2013computer interface (BCI) techniques can result in better stroke patient rehabilitation. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. The preprocessing portion of the framework comprises the use of conventional filters and the independent component analysis (ICA) denoising approach. Fractal dimension (FD) and Hurst exponent (Hur) were then calculated as complexity features, and Tsallis entropy (TsEn) and dispersion entropy (DispEn) were assessed as irregularity parameters. The MI-based BCI features were then statistically retrieved from each participant using two-way analysis of variance (ANOVA) to demonstrate the individuals\u2019 performances from four classes (left hand, right hand, foot, and tongue). The dimensionality reduction algorithm, Laplacian Eigenmap (LE), was used to enhance the MI-based BCI classification performance. Utilizing k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) classifiers, the groups of post-stroke patients were ultimately determined. The findings show that LE with RF and KNN obtained 74.48% and 73.20% accuracy, respectively; therefore, the integrated set of the proposed features along with ICA denoising technique can exactly describe the proposed MI framework, which may be used to explore the four classes of MI-based BCI rehabilitation. This study will help clinicians, doctors, and technicians make a good rehabilitation program for people who have had a stroke.<\/jats:p>","DOI":"10.3390\/s23083889","type":"journal-article","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T02:08:11Z","timestamp":1681265291000},"page":"3889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["EEG Signal Complexity Measurements to Enhance BCI-Based Stroke Patients\u2019 Rehabilitation"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6566-3452","authenticated-orcid":false,"given":"Noor Kamal","family":"Al-Qazzaz","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5461-6746","authenticated-orcid":false,"given":"Alaa A.","family":"Aldoori","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4819-863X","authenticated-orcid":false,"given":"Sawal Hamid Bin Mohd","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Selangor, Malaysia"},{"name":"Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1759-0118","authenticated-orcid":false,"given":"Siti Anom","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM, Serdang 43400, Selangor, Malaysia"},{"name":"Malaysian Research Institute of Ageing (MyAgeing)<sup>TM<\/sup>, University Putra Malaysia, Serdang 43400, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed Kazem","family":"Mohammed","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mustafa Ibrahim","family":"Mohyee","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,11]]},"reference":[{"key":"ref_1","first-page":"1677","article-title":"Cognitive impairment and memory dysfunction after a stroke diagnosis: A post-stroke memory assessment","volume":"10","author":"Ali","year":"2014","journal-title":"Neuropsychiatr. 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