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

Chatterjee, Bipra and Ramaswamy, Palaniappan and Gupta, Cota Navin (2019) Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset. In: Henriques, Jorge and Neves, Nuno and Carvalho, Paolo de, eds. XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. IFMBE Proceedings . Springer, Cham, Switzerland, pp. 1894-1898. ISBN 978-3-030-31634-1. E-ISBN 978-3-030-31635-8. (doi:10.1007/978-3-030-31635-8_231) (KAR id:79906)

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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%.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-030-31635-8_231
Uncontrolled keywords: Brain-Computer Interfaces, CNN, BLDA, RUSBoosting
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Palaniappan Ramaswamy
Date Deposited: 31 Jan 2020 13:27 UTC
Last Modified: 16 Feb 2021 14:11 UTC
Resource URI: (The current URI for this page, for reference purposes)
Ramaswamy, Palaniappan:
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