Mining multi-electrode and multi-wave electroencephalogram based time-interval temporal patterns for improved classification capabilities and explainability.
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| Titel: | Mining multi-electrode and multi-wave electroencephalogram based time-interval temporal patterns for improved classification capabilities and explainability. |
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| Autoren: | Landau O; Malware Lab, Cyber Security Research Center, Ben-Gurion University of the Negev, Israel; Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Israel., Nissim N; Malware Lab, Cyber Security Research Center, Ben-Gurion University of the Negev, Israel; Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Israel. Electronic address: nirni@bgu.ac.il. |
| Quelle: | Artificial intelligence in medicine [Artif Intell Med] 2025 Dec; Vol. 170, pp. 103269. Date of Electronic Publication: 2025 Sep 14. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Elsevier Science Publishing Country of Publication: Netherlands NLM ID: 8915031 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-2860 (Electronic) Linking ISSN: 09333657 NLM ISO Abbreviation: Artif Intell Med Subsets: MEDLINE |
| Imprint Name(s): | Publication: Amsterdam : Elsevier Science Publishing Original Publication: Tecklenburg, Federal Republic of Germany : Burgverlag, c1989- |
| MeSH-Schlagworte: | Electroencephalography*/methods , Algorithms* , Brain-Computer Interfaces* , Data Mining*/methods , Signal Processing, Computer-Assisted* , Brain*/physiology, Humans ; Electrodes ; Time Factors |
| Abstract: | Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Brain-computer interface (BCI) systems, and particularly electroencephalogram (EEG) based BCI systems, have become more widely used in recent years and are utilized in various applications and domains ranging from medicine and marketing to games and entertainment. While different algorithms have been used to analyze EEG data and enable its classification, existing algorithms have two main drawbacks; both their classification and explainability capabilities are limited. Lacking in explainability, they cannot indicate which electrodes and waves led to a classification decision or explain how areas and frequencies of the brain's activity correlate to a specific task. In this study, we propose a novel extension for the time-interval temporal patterns mining algorithms aimed at enhancing the data mining process by enabling a richer set of patterns to be learned from the EEG data, thereby contributing to improved classification and explainability capabilities. The extended algorithm is designed to capture and leverage the unique nature of EEG data by decomposing it into different brain waves and modeling the relations among them and between different electrodes. Our evaluation of the proposed extended algorithm on multiple learning tasks and three EEG datasets demonstrated the extended algorithm's ability to mine richer patterns that improve the classification performance by 4-11 % based on the Area-Under the receiver operating characteristic Curve (AUC) metric, compared to the original version of the algorithm. Moreover, the algorithm was shown to shed light on the areas and frequencies of the brain's activity that are correlated with specific tasks. (Copyright © 2025 Elsevier B.V. All rights reserved.) |
| Contributed Indexing: | Keywords: Brain-computer interface; Classification; Electroencephalogram; Explainability; Multivariate time series data; Time interval mining |
| Entry Date(s): | Date Created: 20250920 Date Completed: 20251030 Latest Revision: 20251030 |
| Update Code: | 20251031 |
| DOI: | 10.1016/j.artmed.2025.103269 |
| PMID: | 40974874 |
| Datenbank: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />Brain-computer interface (BCI) systems, and particularly electroencephalogram (EEG) based BCI systems, have become more widely used in recent years and are utilized in various applications and domains ranging from medicine and marketing to games and entertainment. While different algorithms have been used to analyze EEG data and enable its classification, existing algorithms have two main drawbacks; both their classification and explainability capabilities are limited. Lacking in explainability, they cannot indicate which electrodes and waves led to a classification decision or explain how areas and frequencies of the brain's activity correlate to a specific task. In this study, we propose a novel extension for the time-interval temporal patterns mining algorithms aimed at enhancing the data mining process by enabling a richer set of patterns to be learned from the EEG data, thereby contributing to improved classification and explainability capabilities. The extended algorithm is designed to capture and leverage the unique nature of EEG data by decomposing it into different brain waves and modeling the relations among them and between different electrodes. Our evaluation of the proposed extended algorithm on multiple learning tasks and three EEG datasets demonstrated the extended algorithm's ability to mine richer patterns that improve the classification performance by 4-11 % based on the Area-Under the receiver operating characteristic Curve (AUC) metric, compared to the original version of the algorithm. Moreover, the algorithm was shown to shed light on the areas and frequencies of the brain's activity that are correlated with specific tasks.<br /> (Copyright © 2025 Elsevier B.V. All rights reserved.) |
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| ISSN: | 1873-2860 |
| DOI: | 10.1016/j.artmed.2025.103269 |