Automated Classification of Mental Arithmetic Tasks Using Recurrent Neural Network and Entropy Features Obtained from Multi-Channel EEG Signals

Automated Classification of Mental Arithmetic Tasks Using Recurrent Neural Network and Entropy Features Obtained from Multi-Channel EEG Signals

The automated classification of cognitive workload tasks based on the analysis of multi-channel EEG signals is vital for human–computer interface (HCI) applications. In this paper, we propose a computerized approach for categorizing mental-arithmetic-based cognitive workload tasks using multi-channe...

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Bibliographic Details
Main Authors: Abhishek Varshney, Samit Kumar Ghosh, Sibasankar Padhy, Rajesh Kumar Tripathy, U. Rajendra Acharya
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Electronics
Subjects:
mental arithmetic task
multi-channel EEG
entropies
slope entropy
recurrent neural networks
accuracy
Online Access:https://www.mdpi.com/2079-9292/10/9/1079

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https://www.mdpi.com/2079-9292/10/9/1079

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