Applying Multiple Functional Connectivity Features in GCN for  EEG-Based Human Identification

Tian, Wenli; Li, Ming; Ju, Xiangyu; Liu, Yadong

doi:10.3390/brainsci12081072

Cited by 8 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the adjacency matrix of the GCN graph features of EEG signals, compared with the spatial position relationship between the EEG channels [ 13 , 14 , 15 ] and functional-connectivity-based adjacency matrices [ 16 , 17 , 19 ], GC-based adjacency matrices provide potential possibility (i.e., offering more direction information) to improve the recognition accuracy for the emotion-based system. In our work, the GC-based GCN graph feature shows the superiority to the other matrices, which is consistent with the reference [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

“…The adjacency matrices of the current GCN method is mainly divided into two categories: One uses the spatial position relationship between the EEG channels [ 13 , 14 , 15 ], such as the typical Gaussian kernel function [ 13 ]. The other uses the connectivity of the brain [ 16 , 17 , 18 ], which can be further divided into functional connectivity and effective connectivity [ 19 , 20 , 21 , 22 , 23 ]. Functional connectivity is defined as the statistical interdependence among the EEG signals; most of the current research adopts functional connectivity matrices, such as the phase-locked value (PLV), Pearson correlation coefficient, and mutual information, as the adjacency matrices [ 16 , 17 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…The other uses the connectivity of the brain [ 16 , 17 , 18 ], which can be further divided into functional connectivity and effective connectivity [ 19 , 20 , 21 , 22 , 23 ]. Functional connectivity is defined as the statistical interdependence among the EEG signals; most of the current research adopts functional connectivity matrices, such as the phase-locked value (PLV), Pearson correlation coefficient, and mutual information, as the adjacency matrices [ 16 , 17 , 19 ]. However, functional-connectivity-based adjacency matrices are symmetrical and ignore the directionality of the transmission of information in the brain.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition

Zhang

Chen

et al. 2022

Brain Sciences

View full text Add to dashboard Cite

Graph convolutional neural networks (GCN) have attracted much attention in the task of electroencephalogram (EEG) emotion recognition. However, most features of current GCNs do not take full advantage of the causal connection between the EEG signals in different frequency bands during the process of constructing the adjacency matrix. Based on the causal connectivity between the EEG channels obtained by Granger causality (GC) analysis, this paper proposes a multi-frequency band EEG graph feature extraction and fusion method for EEG emotion recognition. First, the original GC matrices between the EEG signals at each frequency band are calculated via GC analysis, and then they are adaptively converted to asymmetric binary GC matrices through an optimal threshold. Then, a kind of novel GC-based GCN feature (GC-GCN) is constructed by using differential entropy features and the binary GC matrices as the node values and adjacency matrices, respectively. Finally, on the basis of the GC-GCN features, a new multi-frequency band feature fusion method (GC-F-GCN) is proposed, which integrates the graph information of the EEG signals at different frequency bands for the same node. The experimental results demonstrate that the proposed GC-F-GCN method achieves better recognition performance than the state-of-the-art GCN methods, for which average accuracies of 97.91%, 98.46%, and 98.15% were achieved for the arousal, valence, and arousal–valence classifications, respectively.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition

Zhang

Chen

et al. 2022

Brain Sciences

View full text Add to dashboard Cite

show abstract

“…Their proposed method showed that the performance and training time vary with the number of specific channels used to classify the signals. A graph convolutional neural network (GCNN) approach has been adopted by Tian et al 21 to classify EEG signals and identify humans. Their method combined different connectivities, which improved the model's performance and provided an accuracy of 98.56%.…”

Section: Related Workmentioning

confidence: 99%

EEG‐based biometric authentication system using convolutional neural network for military applications

Vadher,

Patel,

Nair

et al. 2023

Security and Privacy

View full text Add to dashboard Cite

In this technological era, as the need for security arises, the use of biometrics is increasing in authentication systems as a secure and convenient method of human identification and verification. Electroencephalogram (EEG) signals have gained significant attention among the various biometric modalities available because of their unique and unforgeable characteristics. In this study, we have proposed an EEG‐based multi‐subject and multi‐task biometric authentication system for the military applications that address the challenges associated with multi‐task variation in EEG signals. The proposed work considers the use of respective EEG signals for the access of artillery, entrance to highly confidential places for the military and so forth by authenticated personnel only. We have used a multi‐subject, multi‐session, and multi‐task () dataset. The dataset was partially preprocessed with basic signal processing techniques such as bad channel repairing, independent component analysis for artifact removal, downsampling to 250 Hz, and an audio filter of 0.01–200 Hz for signal improvisation. This partially preprocessed dataset was further processed and was used in our deep learning model (DL) architectures. For EEG‐based biometric authentication, convolutional neural network (CNN) outperforms many of the state‐of‐the‐art DL architectures with a validation accuracy of approximately 99.86%, training accuracy of 98.49% and precision, recall and F1‐score with values of 99.91% that makes this EEG‐based approach for authentication more reliable. The DL models were also compared based on training and inference time, where CNN used the most training time but took the least time to predict the output. We compared the performance of the CNN model for three preprocessing techniques by feeding mel spectrograms, chromagrams and mel frequency cepstral coefficients, out of which mel spectrograms provided better results. This proposed architecture proves to be robust and efficient for military applications.

show abstract

“…Despite the above‐described effects of volume conduction, a wide range of EEG FC studies applied uncorrected FC measures. In the context of brain fingerprinting (biomarkers accurately identifying participants; e.g., Finn et al, 2015 ), several EEG FC studies utilized or recommended uncorrected FC measures for individual identification both for resting‐state data as well as data collected under various task conditions (spectral coherence: Garau et al, 2016 ; La Rocca et al, 2014 ; phase synchrony: Fraschini et al, 2018 , 2019 ; Kong et al, 2017 ; Kumar et al, 2022 ; Tian et al, 2022 ; Wang, El‐Fiqi, et al, 2019 ; Wang et al, 2020 ; Granger causality estimation: Min et al, 2017 ). Overall, these studies reported high (in the range of 90–100%) individual recognition or identification accuracy with uncorrected measures, providing evidence for the robustness of EEG FC fingerprints.…”

Section: Introductionmentioning

confidence: 99%

The effects of spatial leakage correction on the reliability ofEEG‐based functional connectivity networks

Nagy,

Tóth,

Winkler

et al. 2024

Human Brain Mapping

View full text Add to dashboard Cite

Electroencephalography (EEG) functional connectivity (FC) estimates are confounded by the volume conduction problem. This effect can be greatly reduced by applying FC measures insensitive to instantaneous, zero‐lag dependencies (corrected measures). However, numerous studies showed that FC measures sensitive to volume conduction (uncorrected measures) exhibit higher reliability and higher subject‐level identifiability. We tested how source reconstruction contributed to the reliability difference of EEG FC measures on a large (n = 201) resting‐state data set testing eight FC measures (including corrected and uncorrected measures). We showed that the high reliability of uncorrected FC measures in resting state partly stems from source reconstruction: idiosyncratic noise patterns define a baseline resting‐state functional network that explains a significant portion of the reliability of uncorrected FC measures. This effect remained valid for template head model‐based, as well as individual head model‐based source reconstruction. Based on our findings we made suggestions how to best use spatial leakage corrected and uncorrected FC measures depending on the main goals of the study.

show abstract

Applying Multiple Functional Connectivity Features in GCN for EEG-Based Human Identification

Cited by 8 publications

References 25 publications

Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition

Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition

EEG‐based biometric authentication system using convolutional neural network for military applications

The effects of spatial leakage correction on the reliability ofEEG‐based functional connectivity networks

Contact Info

Product

Resources

About