{"title":"基于硬边界的神经反馈训练程序:一种改进的固定阈值法,在神经反馈训练中更准确地指导目标区域内的受试者。","authors":"Nasrin Sho'ouri","doi":"10.1177/15500594221100159","DOIUrl":null,"url":null,"abstract":"<p><p>In nearly all studies within the domain of neurofeedback, a threshold has been defined for each training feature in a way that subjects' status can be evaluated during training according to the given value. In this study, a hard boundary-based neurofeedback training (HBNFT) method based on the determination of decision boundary using support vector machine (SVM) classifier was proposed in which subjects' status were clarified considering a decision boundary and they could also be encouraged once entering a target area. In this method, a scoring index (SI) was similarly defined whose value was determined in accordance with subject performance during training. The results revealed that employing a classifier and determining a decision boundary instead of using a threshold could prove more successful in accurately guiding them towards a target area and also meet no needs to choose a basis for determining a threshold. Moreover, it was likely that the proposed method could be more efficient in controlling features and preventing extreme changes compared to those using variable thresholds.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Hard Boundary-Based Neurofeedback Training Procedure: A Modified Fixed Thresholding Method for More Accurate Guidance of Subjects Within Target Areas During Neurofeedback Training.\",\"authors\":\"Nasrin Sho'ouri\",\"doi\":\"10.1177/15500594221100159\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In nearly all studies within the domain of neurofeedback, a threshold has been defined for each training feature in a way that subjects' status can be evaluated during training according to the given value. In this study, a hard boundary-based neurofeedback training (HBNFT) method based on the determination of decision boundary using support vector machine (SVM) classifier was proposed in which subjects' status were clarified considering a decision boundary and they could also be encouraged once entering a target area. In this method, a scoring index (SI) was similarly defined whose value was determined in accordance with subject performance during training. The results revealed that employing a classifier and determining a decision boundary instead of using a threshold could prove more successful in accurately guiding them towards a target area and also meet no needs to choose a basis for determining a threshold. Moreover, it was likely that the proposed method could be more efficient in controlling features and preventing extreme changes compared to those using variable thresholds.</p>\",\"PeriodicalId\":10682,\"journal\":{\"name\":\"Clinical EEG and Neuroscience\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical EEG and Neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/15500594221100159\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical EEG and Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/15500594221100159","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Hard Boundary-Based Neurofeedback Training Procedure: A Modified Fixed Thresholding Method for More Accurate Guidance of Subjects Within Target Areas During Neurofeedback Training.
In nearly all studies within the domain of neurofeedback, a threshold has been defined for each training feature in a way that subjects' status can be evaluated during training according to the given value. In this study, a hard boundary-based neurofeedback training (HBNFT) method based on the determination of decision boundary using support vector machine (SVM) classifier was proposed in which subjects' status were clarified considering a decision boundary and they could also be encouraged once entering a target area. In this method, a scoring index (SI) was similarly defined whose value was determined in accordance with subject performance during training. The results revealed that employing a classifier and determining a decision boundary instead of using a threshold could prove more successful in accurately guiding them towards a target area and also meet no needs to choose a basis for determining a threshold. Moreover, it was likely that the proposed method could be more efficient in controlling features and preventing extreme changes compared to those using variable thresholds.
期刊介绍:
Clinical EEG and Neuroscience conveys clinically relevant research and development in electroencephalography and neuroscience. Original articles on any aspect of clinical neurophysiology or related work in allied fields are invited for publication.