ReviewCognitive neurodynamics2021
Complex networks and deep learning for EEG signal analysis.
Review in Cognitive neurodynamics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of EEG-based biomarkers for depression, anxiety, and bipolar disorder: trends in explainable artificial intelligence (XAI).BMC psychiatry · 2025Pooled it
- Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023.Frontiers in physiology · 2023Pooled it
- Research on depression diagnosis method based on multi-scale analysis of frontal lead EEG.BMC medical informatics and decision making · 2026Article
- Deep neural networks and stochastic methods for cognitive modeling of rat behavioral dynamics inCognitive neurodynamics · 2025Article
- EEG-Based Personal Identification by Special Design Domain-Adaptive Autoencoder.Sensors (Basel, Switzerland) · 2025Article
- Analysis of Epilepsy Treatment Strategies Based on an Astrocyte-Neuron-Coupled Network Model.Brain sciences · 2025Article
- Task-Independent Cognitive Workload Discrimination Based on EEG with Stacked Graph Attention Convolutional Networks.Sensors (Basel, Switzerland) · 2025Article
- Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis.Scientific reports · 2025Article
- Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies.Sensors (Basel, Switzerland) · 2024Review
- Decoding of movement-related cortical potentials at different speeds.Cognitive neurodynamics · 2024Article
- Deep learning-assisted single-atom detection of copper ions by combining click chemistry and fast scan voltammetry.Nature communications · 2024Article
- Setting a double-capacitive neuron coupled with Josephson junction and piezoelectric source.Cognitive neurodynamics · 2024Article
- Automatic detection of Alzheimer's disease from EEG signals using an improved AFS-GA hybrid algorithm.Cognitive neurodynamics · 2024Article
- Attention-based cross-frequency graph convolutional network for driver fatigue estimation.Cognitive neurodynamics · 2024Article
- Classification algorithm for motor imagery fusing CNN and attentional mechanisms based on functional near-infrared spectroscopy brain image.Cognitive neurodynamics · 2024Article
- Inter-hospital moderate and advanced Alzheimer's disease detection through convolutional neural networks.Heliyon · 2024Article
- Mental Workload Classification and Tasks Detection in Multitasking: Deep Learning Insights from EEG Study.Brain sciences · 2024Article
- Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network.Cyborg and bionic systems (Washington, D.C.) · 2024Article
- A novel method of swin transformer with time-frequency characteristics for ECG-based arrhythmia detection.Frontiers in cardiovascular medicine · 2024Article
- Objective assessment of cognitive fatigue: a bibliometric analysis.Frontiers in neuroscience · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electroencephalogram (EEG) signals acquired from brain can provide an effective representation of the human's physiological and pathological states. Up to now, much work has been conducted to study and analyze the EEG signals, aiming at spying the current states or the evolution characteristics of the complex brain system. Considering the complex interactions between different structural and functional brain regions, brain network has received a lot of attention and has made great progress in brain mechanism research. In addition, characterized by autonomous, multi-layer and diversified feature extraction, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, including brain state research. Both of them show strong ability in EEG signal analysis, but the combination of these two theories to solve the difficult classification problems based on EEG signals is still in its infancy. We here review the application of these two theories in EEG signal research, mainly involving brain-computer interface, neurological disorders and cognitive analysis. Furthermore, we also develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition. The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.