Black-white hole pattern: an investigation on the automated chronic neuropathic pain detection using EEG signals
Article
Tasci, Irem, Baygin, Mehmet, Barua, Prabal Datta, Hafeez-Baig, Abdul, Dogan, Sengul, Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendra. 2024. "Black-white hole pattern: an investigation on the automated chronic neuropathic pain detection using EEG signals." Cognitive Neurodynamics. 18 (5), pp. 2193-2210. https://doi.org/10.1007/s11571-024-10078-0
Article Title | Black-white hole pattern: an investigation on the automated chronic neuropathic pain detection using EEG signals |
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ERA Journal ID | 3179 |
Article Category | Article |
Authors | Tasci, Irem, Baygin, Mehmet, Barua, Prabal Datta, Hafeez-Baig, Abdul, Dogan, Sengul, Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendra |
Journal Title | Cognitive Neurodynamics |
Journal Citation | 18 (5), pp. 2193-2210 |
Number of Pages | 18 |
Year | 2024 |
Publisher | Springer |
Place of Publication | Netherlands |
ISSN | 1871-4080 |
1871-4099 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s11571-024-10078-0 |
Web Address (URL) | https://link.springer.com/article/10.1007/s11571-024-10078-0 |
Abstract | Electroencephalography (EEG) signals provide information about the brain activities, this study bridges neuroscience and machine learning by introducing an astronomy-inspired feature extraction model. In this work, we developed a novel feature extraction function, black-white hole pattern (BWHPat) which dynamically selects the most suitable pattern from 14 options. We developed BWHPat in a four-phase feature engineering model, involving multileveled feature extraction, feature selection, classification, and cortex map generation. Textural and statistical features are extracted in the first phase, while tunable q-factor wavelet transform (TQWT) aids in multileveled feature extraction. The second phase employs iterative neighborhood component analysis (INCA) for feature selection, and the k-nearest neighbors (kNN) classifier is applied for classification, yielding channel-specific results. A new cortex map generation model highlights the most active channels using median and intersection functions. Our BWHPat-driven model consistently achieved over 99% classification accuracy across three scenarios using the publicly available EEG pain dataset. Furthermore, a semantic cortex map precisely identifies pain-affected brain regions. This study signifies the contribution to EEG signal classification and neuroscience. The BWHPat pattern establishes a unique link between astronomy and feature extraction, enhancing the understanding of brain activities. |
Keywords | Black-white hole pattern; EEG pain detection; Neuroscience; cortex map |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | Firat University, Turkey |
Erzurum Technical University, Turkey | |
School of Business | |
School of Management and Enterprise | |
National Heart Centre, Singapore | |
Duke-NUS Medical School, Singapore | |
School of Mathematics, Physics and Computing |
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https://research.usq.edu.au/item/z5vx1/black-white-hole-pattern-an-investigation-on-the-automated-chronic-neuropathic-pain-detection-using-eeg-signals
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