Credibility-Adjusted Data-Conscious Clustering Method for Robust EEG Signal Analysis
Clustering neurodata, including electroencephalography (EEG) signals, is crucial for brain-computer interface (BCI) and neurological analysis. However, traditional methods struggle with noise, overlapping distributions, and high-dimensional data. This study presents the Credibility-Adjusted Data Con...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11045428/ |
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| author | Fatemeh Divan Teh Ying Wah Kheng Seang Lim Ali Seyed Shirkhorshidi |
| author_facet | Fatemeh Divan Teh Ying Wah Kheng Seang Lim Ali Seyed Shirkhorshidi |
| author_sort | Fatemeh Divan |
| collection | DOAJ |
| description | Clustering neurodata, including electroencephalography (EEG) signals, is crucial for brain-computer interface (BCI) and neurological analysis. However, traditional methods struggle with noise, overlapping distributions, and high-dimensional data. This study presents the Credibility-Adjusted Data Conscious Clustering Method (CADCCM), an adaptive computational learning model for clustering neurodata, including EEG signals. CADCCM improves clustering robustness by dynamically adjusting assignments through credibility updates and optimizing parameters for better accuracy and stability in noisy, high-dimensional data. CADCCM dynamically adjusts cluster assignments by integrating alpha and beta parameters to balance fuzzy membership and credibility. A grid search framework optimizes clustering parameters, and preprocessing techniques (Fourier Transform, Wavelet Transform, and Gaussian filtering) improve feature separability. The method is benchmarked against traditional and recent Clustering methods across 13 datasets. CADCCM achieves superior clustering performance, consistently outperforming baseline methods in Rand Index (RI), F-score, and Cohen’s Kappa, particularly in noisy datasets. Gaussian filtering further enhances clustering accuracy. GPU acceleration ensures computational feasibility for large-scale neurodata applications. Additionally, CADCCM outperforms other methods by satisfying all clustering properties, including scale invariance, richness, consistency, and order independence. CADCCM bridges the gap between traditional and advanced clustering by introducing credibility-based updates and optimized parameter selection, leading to improved accuracy, robustness, and efficiency. The method holds promise for applications in cognitive state monitoring, neurological disorder detection, and BCI systems. CADCCM enhances the interpretability, reliability, and scalability of clustering. Future research will focus on real-time clustering, adaptive hyperparameter tuning, and deep-learning-based feature extraction. |
| format | Article |
| id | doaj-art-8daec4c4b9094fc1be0ff8aaa6e3b2b8 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8daec4c4b9094fc1be0ff8aaa6e3b2b82025-08-20T03:30:14ZengIEEEIEEE Access2169-35362025-01-011311315511317010.1109/ACCESS.2025.358159811045428Credibility-Adjusted Data-Conscious Clustering Method for Robust EEG Signal AnalysisFatemeh Divan0https://orcid.org/0000-0003-4282-5302Teh Ying Wah1https://orcid.org/0000-0002-3202-7035Kheng Seang Lim2https://orcid.org/0000-0002-2787-2365Ali Seyed Shirkhorshidi3https://orcid.org/0000-0002-2063-8664Department of Information Systems, University of Malaya, Kuala Lumpur, MalaysiaDepartment of Information Systems, University of Malaya, Kuala Lumpur, MalaysiaDepartment of Medicine, University of Malaya, Kuala Lumpur, MalaysiaEntefy Inc., Palo Alto, CA, USAClustering neurodata, including electroencephalography (EEG) signals, is crucial for brain-computer interface (BCI) and neurological analysis. However, traditional methods struggle with noise, overlapping distributions, and high-dimensional data. This study presents the Credibility-Adjusted Data Conscious Clustering Method (CADCCM), an adaptive computational learning model for clustering neurodata, including EEG signals. CADCCM improves clustering robustness by dynamically adjusting assignments through credibility updates and optimizing parameters for better accuracy and stability in noisy, high-dimensional data. CADCCM dynamically adjusts cluster assignments by integrating alpha and beta parameters to balance fuzzy membership and credibility. A grid search framework optimizes clustering parameters, and preprocessing techniques (Fourier Transform, Wavelet Transform, and Gaussian filtering) improve feature separability. The method is benchmarked against traditional and recent Clustering methods across 13 datasets. CADCCM achieves superior clustering performance, consistently outperforming baseline methods in Rand Index (RI), F-score, and Cohen’s Kappa, particularly in noisy datasets. Gaussian filtering further enhances clustering accuracy. GPU acceleration ensures computational feasibility for large-scale neurodata applications. Additionally, CADCCM outperforms other methods by satisfying all clustering properties, including scale invariance, richness, consistency, and order independence. CADCCM bridges the gap between traditional and advanced clustering by introducing credibility-based updates and optimized parameter selection, leading to improved accuracy, robustness, and efficiency. The method holds promise for applications in cognitive state monitoring, neurological disorder detection, and BCI systems. CADCCM enhances the interpretability, reliability, and scalability of clustering. Future research will focus on real-time clustering, adaptive hyperparameter tuning, and deep-learning-based feature extraction.https://ieeexplore.ieee.org/document/11045428/Brain-computer interfacesmachine learningpattern clusteringpattern recognitionsignal processing |
| spellingShingle | Fatemeh Divan Teh Ying Wah Kheng Seang Lim Ali Seyed Shirkhorshidi Credibility-Adjusted Data-Conscious Clustering Method for Robust EEG Signal Analysis IEEE Access Brain-computer interfaces machine learning pattern clustering pattern recognition signal processing |
| title | Credibility-Adjusted Data-Conscious Clustering Method for Robust EEG Signal Analysis |
| title_full | Credibility-Adjusted Data-Conscious Clustering Method for Robust EEG Signal Analysis |
| title_fullStr | Credibility-Adjusted Data-Conscious Clustering Method for Robust EEG Signal Analysis |
| title_full_unstemmed | Credibility-Adjusted Data-Conscious Clustering Method for Robust EEG Signal Analysis |
| title_short | Credibility-Adjusted Data-Conscious Clustering Method for Robust EEG Signal Analysis |
| title_sort | credibility adjusted data conscious clustering method for robust eeg signal analysis |
| topic | Brain-computer interfaces machine learning pattern clustering pattern recognition signal processing |
| url | https://ieeexplore.ieee.org/document/11045428/ |
| work_keys_str_mv | AT fatemehdivan credibilityadjusteddataconsciousclusteringmethodforrobusteegsignalanalysis AT tehyingwah credibilityadjusteddataconsciousclusteringmethodforrobusteegsignalanalysis AT khengseanglim credibilityadjusteddataconsciousclusteringmethodforrobusteegsignalanalysis AT aliseyedshirkhorshidi credibilityadjusteddataconsciousclusteringmethodforrobusteegsignalanalysis |