About: Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset

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type	http://eprints.org/ontology/BookSectionEPrint http://eprints.org/ontology/EPrint Article Book Section
seeAlso	HTML Summary of #79906 Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset
sameAs	Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset
http://www.loc.gov...erms/relators/EDT	Nuno Neves Jorge Henriques Paolo de Carvalho
http://eprints.org/ontology/hasAccepted	Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset (PDF)
http://eprints.org/ontology/hasDocument	Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset (PDF) Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset (Other) Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset (Other) Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset (Other) Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset (Other) Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset (Other)
dc:hasVersion	Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset (PDF)
Title	Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-09-25
Creator	Cota Navin Gupta Bipra Chatterjee Palaniappan Ramaswamy
status	peer reviewed published
Publisher	Springer
abstract	Healthcare field is highly benefited by incorporating BCI for detection and diagnosis of some health related detriment as well as rehabilitation and restoration of certain disabilities. An EEG dataset acquired from 15 high-functioning ASD patients, while they were undergoing a P300 experiment in a virtual reality platform, was analysed in this paper using three algorithms. Performance of Bayes Linear Discriminant Analysis (BLDA) was predominant over Convolutional Neural Network (CNN) and Random Undersampling (RUS) Boosting. BLDA rendered 73% overall accuracy in predicting target and the best accuracy for each subject using CNN or BLDA yielded an overall accuracy of 76%.
Is Part Of	Proceedings of XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 https://kar.kent.ac.uk/id/repository
Subject	QA Mathematics (inc Computing science)
list of authors	https://kar.kent.ac.uk/id/eprint/79906#authors
list of editors	https://kar.kent.ac.uk/id/eprint/79906#editors
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #79906 Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset

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