{"title":"基于定向洛比什的可解释特征工程模型与 TTPat 和 CWINCA,用于脑电图伪像分类","authors":"Turker Tuncer , Sengul Dogan , Mehmet Baygin , Irem Tasci , Bulent Mungen , Burak Tasci , Prabal Datta Barua , U.R. Acharya","doi":"10.1016/j.knosys.2024.112555","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and Objective</h3><div>Electroencephalography (EEG) signals are crucial to decipher various brain activities. However, these EEG signals are subtle and contain various artifacts, which can happen due to various reasons. The main aim of this paper is to develop an explainable novel machine learning model that can identify the cause of these artifacts.</div></div><div><h3>Material and method</h3><div>A new EEG signal dataset was collected to classify various types of artifacts. This dataset contains eight classes: seven are artifacts, and one is the EEG signal without artifacts. A novel feature engineering model has been proposed to classify these artifact classes automatically. This model contains three main steps: (i) feature generation with the proposed transition table pattern (TTPat), (ii) the proposed cumulative weight-based iterative neighborhood component analysis (CWINCA)-based feature selection, and (iii) classification using t algorithm-based k-nearest neighbors (tkNN). The novelty of this work is TTPat feature extractor and CWINCA feature selector. Channel-based transformation is performed using the proposed TTPat, which extracts 392 features from the transformed EEG signal. A novel CWINCA feature selector is proposed. The artifacts are classified using tkNN algorithm.</div></div><div><h3>Results</h3><div>The proposed TTPat and CWINCA-based feature engineering model obtained a classification accuracy ranging from 66.39% to 97.69% for 30 cases. We presented the explainable results using a new symbolic language termed Directed Lobish.</div></div><div><h3>Conclusions</h3><div>The results and findings demonstrated that the proposed explainable feature engineering (EFE) model is good at artifact detection and classification. Directed Lobish has been presented to obtain explainable results and is a new symbolic language.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"305 ","pages":"Article 112555"},"PeriodicalIF":7.2000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Directed Lobish-based explainable feature engineering model with TTPat and CWINCA for EEG artifact classification\",\"authors\":\"Turker Tuncer , Sengul Dogan , Mehmet Baygin , Irem Tasci , Bulent Mungen , Burak Tasci , Prabal Datta Barua , U.R. Acharya\",\"doi\":\"10.1016/j.knosys.2024.112555\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background and Objective</h3><div>Electroencephalography (EEG) signals are crucial to decipher various brain activities. However, these EEG signals are subtle and contain various artifacts, which can happen due to various reasons. The main aim of this paper is to develop an explainable novel machine learning model that can identify the cause of these artifacts.</div></div><div><h3>Material and method</h3><div>A new EEG signal dataset was collected to classify various types of artifacts. This dataset contains eight classes: seven are artifacts, and one is the EEG signal without artifacts. A novel feature engineering model has been proposed to classify these artifact classes automatically. This model contains three main steps: (i) feature generation with the proposed transition table pattern (TTPat), (ii) the proposed cumulative weight-based iterative neighborhood component analysis (CWINCA)-based feature selection, and (iii) classification using t algorithm-based k-nearest neighbors (tkNN). The novelty of this work is TTPat feature extractor and CWINCA feature selector. Channel-based transformation is performed using the proposed TTPat, which extracts 392 features from the transformed EEG signal. A novel CWINCA feature selector is proposed. The artifacts are classified using tkNN algorithm.</div></div><div><h3>Results</h3><div>The proposed TTPat and CWINCA-based feature engineering model obtained a classification accuracy ranging from 66.39% to 97.69% for 30 cases. We presented the explainable results using a new symbolic language termed Directed Lobish.</div></div><div><h3>Conclusions</h3><div>The results and findings demonstrated that the proposed explainable feature engineering (EFE) model is good at artifact detection and classification. Directed Lobish has been presented to obtain explainable results and is a new symbolic language.</div></div>\",\"PeriodicalId\":49939,\"journal\":{\"name\":\"Knowledge-Based Systems\",\"volume\":\"305 \",\"pages\":\"Article 112555\"},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge-Based Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950705124011894\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705124011894","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Directed Lobish-based explainable feature engineering model with TTPat and CWINCA for EEG artifact classification
Background and Objective
Electroencephalography (EEG) signals are crucial to decipher various brain activities. However, these EEG signals are subtle and contain various artifacts, which can happen due to various reasons. The main aim of this paper is to develop an explainable novel machine learning model that can identify the cause of these artifacts.
Material and method
A new EEG signal dataset was collected to classify various types of artifacts. This dataset contains eight classes: seven are artifacts, and one is the EEG signal without artifacts. A novel feature engineering model has been proposed to classify these artifact classes automatically. This model contains three main steps: (i) feature generation with the proposed transition table pattern (TTPat), (ii) the proposed cumulative weight-based iterative neighborhood component analysis (CWINCA)-based feature selection, and (iii) classification using t algorithm-based k-nearest neighbors (tkNN). The novelty of this work is TTPat feature extractor and CWINCA feature selector. Channel-based transformation is performed using the proposed TTPat, which extracts 392 features from the transformed EEG signal. A novel CWINCA feature selector is proposed. The artifacts are classified using tkNN algorithm.
Results
The proposed TTPat and CWINCA-based feature engineering model obtained a classification accuracy ranging from 66.39% to 97.69% for 30 cases. We presented the explainable results using a new symbolic language termed Directed Lobish.
Conclusions
The results and findings demonstrated that the proposed explainable feature engineering (EFE) model is good at artifact detection and classification. Directed Lobish has been presented to obtain explainable results and is a new symbolic language.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.