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Thought-Actuated Wheelchair Navigation with Communication Assistance Using Statistical Cross-Correlation-Based Features and Extreme Learning Machine

BACKGROUND: A simple data collection approach based on electroencephalogram (EEG) measurements has been proposed in this study to implement a brain–computer interface, i.e., thought-controlled wheelchair navigation system with communication assistance. METHOD: The EEG signals are recorded for seven...

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Autores principales: Nataraj, Sathees Kumar, Paulraj, M. P., Yaacob, Sazali Bin, Adom, Abdul Hamid Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7866946/
https://www.ncbi.nlm.nih.gov/pubmed/33575195
http://dx.doi.org/10.4103/jmss.JMSS_52_19
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author Nataraj, Sathees Kumar
Paulraj, M. P.
Yaacob, Sazali Bin
Adom, Abdul Hamid Bin
author_facet Nataraj, Sathees Kumar
Paulraj, M. P.
Yaacob, Sazali Bin
Adom, Abdul Hamid Bin
author_sort Nataraj, Sathees Kumar
collection PubMed
description BACKGROUND: A simple data collection approach based on electroencephalogram (EEG) measurements has been proposed in this study to implement a brain–computer interface, i.e., thought-controlled wheelchair navigation system with communication assistance. METHOD: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd-ball paradigm. The proposed system records EEG signals from 10 individuals at eight-channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross-correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross-correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures (“minimum,” “mean,” “maximum,” and “standard deviation”) were derived from the cross-correlated signals. Finally, the extracted feature sets were validated through online sequential-extreme learning machine algorithm. RESULTS AND CONCLUSION: The results of the classification networks were compared with each set of features, and the results indicated that μ (r) feature set based on cross-correlation signals had the best performance with a recognition rate of 91.93%.
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spelling pubmed-78669462021-02-10 Thought-Actuated Wheelchair Navigation with Communication Assistance Using Statistical Cross-Correlation-Based Features and Extreme Learning Machine Nataraj, Sathees Kumar Paulraj, M. P. Yaacob, Sazali Bin Adom, Abdul Hamid Bin J Med Signals Sens Original Article BACKGROUND: A simple data collection approach based on electroencephalogram (EEG) measurements has been proposed in this study to implement a brain–computer interface, i.e., thought-controlled wheelchair navigation system with communication assistance. METHOD: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd-ball paradigm. The proposed system records EEG signals from 10 individuals at eight-channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross-correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross-correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures (“minimum,” “mean,” “maximum,” and “standard deviation”) were derived from the cross-correlated signals. Finally, the extracted feature sets were validated through online sequential-extreme learning machine algorithm. RESULTS AND CONCLUSION: The results of the classification networks were compared with each set of features, and the results indicated that μ (r) feature set based on cross-correlation signals had the best performance with a recognition rate of 91.93%. Wolters Kluwer - Medknow 2020-11-11 /pmc/articles/PMC7866946/ /pubmed/33575195 http://dx.doi.org/10.4103/jmss.JMSS_52_19 Text en Copyright: © 2020 Journal of Medical Signals & Sensors http://creativecommons.org/licenses/by-nc-sa/4.0 This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle Original Article
Nataraj, Sathees Kumar
Paulraj, M. P.
Yaacob, Sazali Bin
Adom, Abdul Hamid Bin
Thought-Actuated Wheelchair Navigation with Communication Assistance Using Statistical Cross-Correlation-Based Features and Extreme Learning Machine
title Thought-Actuated Wheelchair Navigation with Communication Assistance Using Statistical Cross-Correlation-Based Features and Extreme Learning Machine
title_full Thought-Actuated Wheelchair Navigation with Communication Assistance Using Statistical Cross-Correlation-Based Features and Extreme Learning Machine
title_fullStr Thought-Actuated Wheelchair Navigation with Communication Assistance Using Statistical Cross-Correlation-Based Features and Extreme Learning Machine
title_full_unstemmed Thought-Actuated Wheelchair Navigation with Communication Assistance Using Statistical Cross-Correlation-Based Features and Extreme Learning Machine
title_short Thought-Actuated Wheelchair Navigation with Communication Assistance Using Statistical Cross-Correlation-Based Features and Extreme Learning Machine
title_sort thought-actuated wheelchair navigation with communication assistance using statistical cross-correlation-based features and extreme learning machine
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7866946/
https://www.ncbi.nlm.nih.gov/pubmed/33575195
http://dx.doi.org/10.4103/jmss.JMSS_52_19
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