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A Hierarchical Meta-model for Multi-Class Mental Task Based Brain-Computer Interfaces

Akshansh, Gupta and R. K., Agrawal and Jyoti Singh, Kirar and Baljeet, Kaur and Weiping, Ding and Chin-Teng, Lin and Andreu-Perez, Javier and Mukesh, Prasad (2020) 'A Hierarchical Meta-model for Multi-Class Mental Task Based Brain-Computer Interfaces.' Neurocomputing, 389. pp. 207-217. ISSN 0925-2312

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In the last few years, many research works have been suggested on Brain- Computer Interface (BCI), which assists severely physically disabled persons to communicate directly with the help of electroencephalogram (EEG) signal, generated by the thought process of the brain. Thought generation inside the brain is a dynamic process, and plenty thoughts occur within a small time window. Thus, there is a need for a BCI device that can distinguish these various ideas simultaneously. In this research work, our previous binary-class mental task classication has been extended to the multi-class mental task problem. The present work proposed a novel feature construction scheme for multi mental task classication. In the proposed method, features are

Item Type: Article
Uncontrolled Keywords: Machine Learning; Artificial Intelligence; Brain Computer Interfaces
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 19 Oct 2018 11:25
Last Modified: 23 Sep 2022 19:29

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