Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces
Copyright © 2024 Xue, Song, Wu, Cheng and Pan..
Introduction: Within the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity.
Methods: Inspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification.
Results and discussion: Achievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
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| Enthalten in: |
Frontiers in neuroscience - 18(2024) vom: 03., Seite 1309594 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xue, Qiwei [VerfasserIn] |
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| Links: |
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| Themen: |
Brain computer interface (BCI) |
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| Anmerkungen: |
Date Revised 25.04.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fnins.2024.1309594 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370956222 |
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| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Copyright © 2024 Xue, Song, Wu, Cheng and Pan. | ||
| 520 | |a Introduction: Within the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity | ||
| 520 | |a Methods: Inspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification | ||
| 520 | |a Results and discussion: Achievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Electroencephalography (EEG) | |
| 650 | 4 | |a Graph Convolutional Network (GCN) | |
| 650 | 4 | |a brain computer interface (BCI) | |
| 650 | 4 | |a forward-forward mechanism | |
| 650 | 4 | |a motor imagery (MI) | |
| 700 | 1 | |a Song, Yuntao |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Huapeng |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Yong |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Hongtao |e verfasserin |4 aut | |
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