GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals

Hou, Yimin; Jia, Shuyue; Lun, Xiangmin; Hao, Ziqian; Shi, Yan; Li, Yahui; Zhou, Rui; Lv, Jinglei

doi:10.1109/tnnls.2022.3202569

Cited by 63 publications

(22 citation statements)

References 52 publications

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“…One of the frequently used deep learning techniques for functional/effective brain network classification is the graph convolution network (GCN). GCN has been successfully applied for emotion prediction, brain-computer interface, and other applications (Lun et al, 2020;Zhong et al, 2020;Chen et al, 2021). However, the advantage of various deep learning techniques including GCN is underutilized in electrophysiology-based brain network analysis of dementia-related disorder.…”

Section: Discussionmentioning

confidence: 99%

Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review

Adebisi

Veluvolu²

2023

Front. Aging Neurosci.

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BackgroundDementia-related disorders have been an age-long challenge to the research and healthcare communities as their various forms are expressed with similar clinical symptoms. These disorders are usually irreversible at their late onset, hence their lack of validated and approved cure. Since their prodromal stages usually lurk for a long period of time before the expression of noticeable clinical symptoms, a secondary prevention which has to do with treating the early onsets has been suggested as the possible solution. Connectivity analysis of electrophysiology signals has played significant roles in the diagnosis of various dementia disorders through early onset identification.ObjectiveWith the various applications of electrophysiology signals, the purpose of this study is to systematically review the step-by-step procedures of connectivity analysis frameworks for dementia disorders. This study aims at identifying the methodological issues involved in such frameworks and also suggests approaches to solve such issues.MethodsIn this study, ProQuest, PubMed, IEEE Xplore, Springer Link, and Science Direct databases are employed for exploring the evolution and advancement of connectivity analysis of electrophysiology signals of dementia-related disorders between January 2016 to December 2022. The quality of assessment of the studied articles was done using Cochrane guidelines for the systematic review of diagnostic test accuracy.ResultsOut of a total of 4,638 articles found to have been published on the review scope between January 2016 to December 2022, a total of 51 peer-review articles were identified to completely satisfy the review criteria. An increasing trend of research in this domain is identified within the considered time frame. The ratio of MEG and EEG utilization found within the reviewed articles is 1:8. Most of the reviewed articles employed graph theory metrics for their analysis with clustering coefficient (CC), global efficiency (GE), and characteristic path length (CPL) appearing more frequently compared to other metrics.SignificanceThis study provides general insight into how to employ connectivity measures for the analysis of electrophysiology signals of dementia-related disorders in order to better understand their underlying mechanism and their differential diagnosis.

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Section: Discussionmentioning

confidence: 99%

Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review

Adebisi

Veluvolu²

2023

Front. Aging Neurosci.

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“…The closed-loop feedback control algorithm, fast Fourier analysis [71] Contour reconstruction VR applications [185] LSTM-RNN; [122] Decision Tree þ Continuous Wavelet Transform; [123] E2E CNN with residual block model; [127] GCNs-Net [186] Human emotion recognition; Intention prediction Safe HRC; Interaction with mobile robot; Collaborative manipulation ECG RNN [146] Human emotion recognition HRI EMG HD þ PCA; [138] LDA þ SVM þ Bayesian optimization; [139] LSTM þ RSL; [187] CNN; [188] Hidden semi-Markov model þ Gaussian mixture regression; [143] AGrM; [144] KNN; [105] Modified FOS [172] Hand gesture recognition; Stiffness detection; Precise control on the pinch-type and the corresponding force; Intention recognition Robotic Skill Learning and HRC; Prosthetic hand manipulation; HRC sawing task Physical Body gesture LSTM; [117,134] CNN [134] Hand pose reconstruction; Hand gesture recognition XR applications Strain RBFNN-DTW; [135] CNN þ LSTM [145] Hand gesture recognition; Hand motion detection Human-robot and environment interaction Pressure SVM; [189,190] CNN; [190] KNN; [190] MLP [117] Hand gesture recognition; Standing posture recognition; Holding force detection Safe HRC insightful measures of human emotive and intent-driven states.…”

Section: Pneumatic Actuatormentioning

confidence: 99%

Multimodal Human–Robot Interaction for Human‐Centric Smart Manufacturing: A Survey

Wang,

Zheng,

et al. 2023

Advanced Intelligent Systems

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Human–robot interaction (HRI) has escalated in notability in recent years, and multimodal communication and control strategies are necessitated to guarantee a secure, efficient, and intelligent HRI experience. In spite of the considerable focus on multimodal HRI, comprehensive disquisitions delineating various modalities and intricately analyzing their combinations remain elusive, consequently limiting holistic understanding and future advancements. This article aspires to bridge this inadequacy by conducting a profound exploration of multimodal HRI, predominantly concentrating on four principal modalities: vision, auditory and language, haptics, and physiological sensing. An extensive review encapsulating algorithmic dissection, interface devices, and applicative dimensions forms part of this discourse. This manuscript distinctively combines multimodal HRI with cognitive science, deeply probing into the three dimensions, perception, cognition, and action, thereby demystifying algorithms intrinsic to multimodal HRI. Finally, it accentuates the empirical challenges and contours preemptive trajectories for multimodal HRI in human‐centric smart manufacturing.

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“…Due to their powerful learning and generalization capabilities, artificial neural networks are widely used to deal with pattern recognition problems in the engineering field ( Shi et al, 2008 ; Hou et al, 2020 ). This article uses CNN to extract data features in the data preprocessing part.…”

Section: Related Preliminary Workmentioning

confidence: 99%

Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach

Zhou

Jingru

Wang

et al. 2022

PeerJ Computer Science

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Load forecasting is very essential in the analysis and grid planning of power systems. For this reason, we first propose a household load forecasting method based on federated deep learning and non-intrusive load monitoring (NILM). As far as we know, this is the first research on federated learning (FL) in household load forecasting based on NILM. In this method, the integrated power is decomposed into individual device power by non-intrusive load monitoring, and the power of individual appliances is predicted separately using a federated deep learning model. Finally, the predicted power values of individual appliances are aggregated to form the total power prediction. Specifically, by separately predicting the electrical equipment to obtain the predicted power, it avoids the error caused by the strong time dependence in the power signal of a single device. In the federated deep learning prediction model, the household owners with the power data share the parameters of the local model instead of the local power data, guaranteeing the privacy of the household user data. The case results demonstrate that the proposed approach provides a better prediction effect than the traditional methodology that directly predicts the aggregated signal as a whole. In addition, experiments in various federated learning environments are designed and implemented to validate the validity of this methodology.

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GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals

Cited by 63 publications

References 52 publications

Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review

Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review

Multimodal Human–Robot Interaction for Human‐Centric Smart Manufacturing: A Survey

Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach

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