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Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain—Computer Interfaces

Convolutional neural networks (CNNs) have shown great potential in the field of brain–computer interfaces (BCIs) due to their ability to directly process raw electroencephalogram (EEG) signals without artificial feature extraction. Some CNNs have achieved better classification accuracy than that of...

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Detalles Bibliográficos
Autores principales: Liang, Xinbin, Liu, Yaru, Yu, Yang, Liu, Kaixuan, Liu, Yadong, Zhou, Zongtan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9954538/
https://www.ncbi.nlm.nih.gov/pubmed/36831811
http://dx.doi.org/10.3390/brainsci13020268
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author Liang, Xinbin
Liu, Yaru
Yu, Yang
Liu, Kaixuan
Liu, Yadong
Zhou, Zongtan
author_facet Liang, Xinbin
Liu, Yaru
Yu, Yang
Liu, Kaixuan
Liu, Yadong
Zhou, Zongtan
author_sort Liang, Xinbin
collection PubMed
description Convolutional neural networks (CNNs) have shown great potential in the field of brain–computer interfaces (BCIs) due to their ability to directly process raw electroencephalogram (EEG) signals without artificial feature extraction. Some CNNs have achieved better classification accuracy than that of traditional methods. Raw EEG signals are usually represented as a two-dimensional (2-D) matrix composed of channels and time points, ignoring the spatial topological information of electrodes. Our goal is to make a CNN that takes raw EEG signals as inputs have the ability to learn spatial topological features and improve its classification performance while basically maintaining its original structure. We propose an EEG topographic representation module (TRM). This module consists of (1) a mapping block from raw EEG signals to a 3-D topographic map and (2) a convolution block from the topographic map to an output with the same size as the input. According to the size of the convolutional kernel used in the convolution block, we design two types of TRMs, namely TRM-(5,5) and TRM-(3,3). We embed the two TRM types into three widely used CNNs (ShallowConvNet, DeepConvNet and EEGNet) and test them on two publicly available datasets (the Emergency Braking During Simulated Driving Dataset (EBDSDD) and the High Gamma Dataset (HGD)). Results show that the classification accuracies of all three CNNs are improved on both datasets after using the TRMs. With TRM-(5,5), the average classification accuracies of DeepConvNet, EEGNet and ShallowConvNet are improved by 6.54%, 1.72% and 2.07% on the EBDSDD and by 6.05%, 3.02% and 5.14% on the HGD, respectively; with TRM-(3,3), they are improved by 7.76%, 1.71% and 2.17% on the EBDSDD and by 7.61%, 5.06% and 6.28% on the HGD, respectively. We improve the classification performance of three CNNs on both datasets through the use of TRMs, indicating that they have the capability to mine spatial topological EEG information. More importantly, since the output of a TRM has the same size as the input, CNNs with raw EEG signals as inputs can use this module without changing their original structures.
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spelling pubmed-99545382023-02-25 Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain—Computer Interfaces Liang, Xinbin Liu, Yaru Yu, Yang Liu, Kaixuan Liu, Yadong Zhou, Zongtan Brain Sci Article Convolutional neural networks (CNNs) have shown great potential in the field of brain–computer interfaces (BCIs) due to their ability to directly process raw electroencephalogram (EEG) signals without artificial feature extraction. Some CNNs have achieved better classification accuracy than that of traditional methods. Raw EEG signals are usually represented as a two-dimensional (2-D) matrix composed of channels and time points, ignoring the spatial topological information of electrodes. Our goal is to make a CNN that takes raw EEG signals as inputs have the ability to learn spatial topological features and improve its classification performance while basically maintaining its original structure. We propose an EEG topographic representation module (TRM). This module consists of (1) a mapping block from raw EEG signals to a 3-D topographic map and (2) a convolution block from the topographic map to an output with the same size as the input. According to the size of the convolutional kernel used in the convolution block, we design two types of TRMs, namely TRM-(5,5) and TRM-(3,3). We embed the two TRM types into three widely used CNNs (ShallowConvNet, DeepConvNet and EEGNet) and test them on two publicly available datasets (the Emergency Braking During Simulated Driving Dataset (EBDSDD) and the High Gamma Dataset (HGD)). Results show that the classification accuracies of all three CNNs are improved on both datasets after using the TRMs. With TRM-(5,5), the average classification accuracies of DeepConvNet, EEGNet and ShallowConvNet are improved by 6.54%, 1.72% and 2.07% on the EBDSDD and by 6.05%, 3.02% and 5.14% on the HGD, respectively; with TRM-(3,3), they are improved by 7.76%, 1.71% and 2.17% on the EBDSDD and by 7.61%, 5.06% and 6.28% on the HGD, respectively. We improve the classification performance of three CNNs on both datasets through the use of TRMs, indicating that they have the capability to mine spatial topological EEG information. More importantly, since the output of a TRM has the same size as the input, CNNs with raw EEG signals as inputs can use this module without changing their original structures. MDPI 2023-02-05 /pmc/articles/PMC9954538/ /pubmed/36831811 http://dx.doi.org/10.3390/brainsci13020268 Text en © 2023 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
Liang, Xinbin
Liu, Yaru
Yu, Yang
Liu, Kaixuan
Liu, Yadong
Zhou, Zongtan
Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain—Computer Interfaces
title Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain—Computer Interfaces
title_full Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain—Computer Interfaces
title_fullStr Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain—Computer Interfaces
title_full_unstemmed Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain—Computer Interfaces
title_short Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain—Computer Interfaces
title_sort convolutional neural network with a topographic representation module for eeg-based brain—computer interfaces
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9954538/
https://www.ncbi.nlm.nih.gov/pubmed/36831811
http://dx.doi.org/10.3390/brainsci13020268
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