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Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces

Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain–computer interface (SSVEP-based BCI), whi...

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Autores principales: Chuang, Chia-Chun, Lee, Chien-Ching, So, Edmund-Cheung, Yeng, Chia-Hong, Chen, Yeou-Jiunn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9656443/
https://www.ncbi.nlm.nih.gov/pubmed/36366001
http://dx.doi.org/10.3390/s22218303
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author Chuang, Chia-Chun
Lee, Chien-Ching
So, Edmund-Cheung
Yeng, Chia-Hong
Chen, Yeou-Jiunn
author_facet Chuang, Chia-Chun
Lee, Chien-Ching
So, Edmund-Cheung
Yeng, Chia-Hong
Chen, Yeou-Jiunn
author_sort Chuang, Chia-Chun
collection PubMed
description Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain–computer interface (SSVEP-based BCI), which can help people communicate with others. To ease the operation of the input interface, a single channel-based SSVEP-based BCI is selected. To increase the practicality of SSVEP-based BCI, multi-task learning is adopted to develop the neural network-based intelligent system, which can suppress the noise components and obtain a high level of accuracy of classification. Thus, denoising and classification tasks are selected in multi-task learning. The experimental results show that the proposed multi-task learning can effectively integrate the advantages of denoising and discriminative characteristics and outperform other approaches. Therefore, multi-task learning with denoising and classification tasks is very suitable for developing an SSVEP-based BCI for practical applications. In the future, an augmentative and alternative communication interface can be implemented and examined for helping people with ALS communicate with others in their daily lives.
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spelling pubmed-96564432022-11-15 Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces Chuang, Chia-Chun Lee, Chien-Ching So, Edmund-Cheung Yeng, Chia-Hong Chen, Yeou-Jiunn Sensors (Basel) Article Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain–computer interface (SSVEP-based BCI), which can help people communicate with others. To ease the operation of the input interface, a single channel-based SSVEP-based BCI is selected. To increase the practicality of SSVEP-based BCI, multi-task learning is adopted to develop the neural network-based intelligent system, which can suppress the noise components and obtain a high level of accuracy of classification. Thus, denoising and classification tasks are selected in multi-task learning. The experimental results show that the proposed multi-task learning can effectively integrate the advantages of denoising and discriminative characteristics and outperform other approaches. Therefore, multi-task learning with denoising and classification tasks is very suitable for developing an SSVEP-based BCI for practical applications. In the future, an augmentative and alternative communication interface can be implemented and examined for helping people with ALS communicate with others in their daily lives. MDPI 2022-10-29 /pmc/articles/PMC9656443/ /pubmed/36366001 http://dx.doi.org/10.3390/s22218303 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chuang, Chia-Chun
Lee, Chien-Ching
So, Edmund-Cheung
Yeng, Chia-Hong
Chen, Yeou-Jiunn
Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces
title Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces
title_full Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces
title_fullStr Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces
title_full_unstemmed Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces
title_short Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces
title_sort multi-task learning-based deep neural network for steady-state visual evoked potential-based brain–computer interfaces
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9656443/
https://www.ncbi.nlm.nih.gov/pubmed/36366001
http://dx.doi.org/10.3390/s22218303
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