ArticleSensors (Basel, Switzerland)2024
Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neuroimage analysis using artificial intelligence approaches: a systematic review.Medical & biological engineering & computing · 2024Pooled it
- Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.Neural regeneration research · 2026Article
- Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2026Review
- Unified Temporal-Spectral-Spatial Modeling for Robust and Generalizable Motor Imagery Brain-Computer Interfaces.Bioengineering (Basel, Switzerland) · 2026Article
- Bridging Traditional Modeling and Artificial Intelligence in Measles Epidemiology: Methods, Applications, and Future Directions-A Narrative Review.Journal of clinical medicine · 2026Review
- A multimodal explainable artificial intelligence framework for interpretable Parkinson's disease prediction.Scientific reports · 2026Article
- Electroencephalography for early Alzheimer's disease diagnosis: from advanced feature engineering to interpretable ai and clinical translation.Frontiers in psychiatry · 2026Review
- Generalizable Hybrid Wavelet-Deep Learning Architecture for Robust Arrhythmia Detection in Wearable ECG Monitoring.Sensors (Basel, Switzerland) · 2025Article
- The Next Frontier in Brain Monitoring: A Comprehensive Look at In-Ear EEG Electrodes and Their Applications.Sensors (Basel, Switzerland) · 2025Review
- Classification of Electroencephalography Motor Execution Signals Using a Hybrid Neural Network Based on Instantaneous Frequency and Amplitude Obtained via Empirical Wavelet Transform.Sensors (Basel, Switzerland) · 2025Article
- Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics.Scientific reports · 2025Article
- GEM-CRAP: a fusion architecture for focal seizure detection.Journal of translational medicine · 2025Article
- Determination of the Time-frequency Features for Impulse Components in EEG Signals.Neuroinformatics · 2025Article
- Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.Journal of clinical medicine · 2025Review
- What does my network learn? Assessing interpretability of deep learning for EEG.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Dynamic reconstruction of electroencephalogram data using RBF neural networks.Frontiers in neuroscience · 2025Article
- Spatial Cognitive EEG Feature Extraction and Classification Based on MSSECNN and PCMI.Bioengineering (Basel, Switzerland) · 2024Article
- Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems.Sensors (Basel, Switzerland) · 2024Article
- Schizophrenia Detection and Classification: A Systematic Review of the Last Decade.Diagnostics (Basel, Switzerland) · 2024Review
- A Lightweight Multi-Mental Disorders Detection Method Using Entropy-Based Matrix from Single-Channel EEG Signals.Brain sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.
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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.