EEG signal complexity measurements to enhance BCI-based stroke patients' rehabilitation
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–computer interface (BCI) techniques can result in better stroke patient rehabilitation. This study used the proposed motor imagery (MI) framework to analyze the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Multidisciplinary Digital Publishing Institute
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/107446/1/EEG%20Signal%20Complexity%20Measurements%20to%20Enhance%20BCI-Based%20Stroke%20Patients%E2%80%99%20Rehabilitation.pdf |
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| author | Al-Qazzaz, Noor Kamal Aldoori, Alaa A. Mohd Ali, Sawal Hamid Ahmad, Siti Anom Mohammed, Ahmed Kazem Mohyee, Mustafa Ibrahim |
| author_facet | Al-Qazzaz, Noor Kamal Aldoori, Alaa A. Mohd Ali, Sawal Hamid Ahmad, Siti Anom Mohammed, Ahmed Kazem Mohyee, Mustafa Ibrahim |
| author_sort | Al-Qazzaz, Noor Kamal |
| collection | UPM |
| description | 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–computer 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’ 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. |
| first_indexed | 2024-12-09T02:18:54Z |
| format | Article |
| id | upm.eprints-107446 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-12-09T02:18:54Z |
| publishDate | 2023 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | upm.eprints-1074462024-10-17T07:47:03Z http://psasir.upm.edu.my/id/eprint/107446/ EEG signal complexity measurements to enhance BCI-based stroke patients' rehabilitation Al-Qazzaz, Noor Kamal Aldoori, Alaa A. Mohd Ali, Sawal Hamid Ahmad, Siti Anom Mohammed, Ahmed Kazem Mohyee, Mustafa Ibrahim 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–computer 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’ 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. Multidisciplinary Digital Publishing Institute 2023-04-11 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/107446/1/EEG%20Signal%20Complexity%20Measurements%20to%20Enhance%20BCI-Based%20Stroke%20Patients%E2%80%99%20Rehabilitation.pdf Al-Qazzaz, Noor Kamal and Aldoori, Alaa A. and Mohd Ali, Sawal Hamid and Ahmad, Siti Anom and Mohammed, Ahmed Kazem and Mohyee, Mustafa Ibrahim (2023) EEG signal complexity measurements to enhance BCI-based stroke patients' rehabilitation. Sensors, 23 (8). art. no. 3889. pp. 1-24. ISSN 1424-8220 https://www.mdpi.com/1424-8220/23/8/3889 10.3390/s23083889 |
| spellingShingle | Al-Qazzaz, Noor Kamal Aldoori, Alaa A. Mohd Ali, Sawal Hamid Ahmad, Siti Anom Mohammed, Ahmed Kazem Mohyee, Mustafa Ibrahim EEG signal complexity measurements to enhance BCI-based stroke patients' rehabilitation |
| title | EEG signal complexity measurements to enhance BCI-based stroke patients' rehabilitation |
| title_full | EEG signal complexity measurements to enhance BCI-based stroke patients' rehabilitation |
| title_fullStr | EEG signal complexity measurements to enhance BCI-based stroke patients' rehabilitation |
| title_full_unstemmed | EEG signal complexity measurements to enhance BCI-based stroke patients' rehabilitation |
| title_short | EEG signal complexity measurements to enhance BCI-based stroke patients' rehabilitation |
| title_sort | eeg signal complexity measurements to enhance bci based stroke patients rehabilitation |
| url | http://psasir.upm.edu.my/id/eprint/107446/1/EEG%20Signal%20Complexity%20Measurements%20to%20Enhance%20BCI-Based%20Stroke%20Patients%E2%80%99%20Rehabilitation.pdf |
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