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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11045428/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |