ReviewIEEE journal of biomedical and health informatics2018
Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks.
Review in IEEE journal of biomedical and health informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline 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
- Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis.BioData mining · 2026Article
- Identification of Bipolar Disorder and Schizophrenia Based on Brain CT and Deep Learning Methods.Journal of imaging informatics in medicine · 2025Article
- Neurophysiological Approaches to Lie Detection: A Systematic Review.Brain sciences · 2025Review
- A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain-Computer Interfaces to Enhance Motor Imagery Classification.Sensors (Basel, Switzerland) · 2025Article
- Review of Predicting Synergistic Drug Combinations.Life (Basel, Switzerland) · 2023Review
- Design of a Sleep Modulation System with FPGA-Accelerated Deep Learning for Closed-loop Stage-Specific In-Phase Auditory Stimulation.IEEE International Symposium on Circuits and Systems proceedings. IEEE International Symposium on Circuits and Systems · 2023Article
- Working memory dysfunction in fibromyalgia is associated with genotypes of the catechol- O-methyltransferase gene: an event-related potential study.European archives of psychiatry and clinical neuroscience · 2023Article
- Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA).AIMS neuroscience · 2023Article
- AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions.Healthcare (Basel, Switzerland) · 2022Review
- Review
- A deep learning algorithm based on 1D CNN-LSTM for automatic sleep staging.Technology and health care : official journal of the European Society for Engineering and Medicine · 2022Article
- PScL-HDeep: image-based prediction of protein subcellular location in human tissue using ensemble learning of handcrafted and deep learned features with two-layer feature selection.Briefings in bioinformatics · 2021Article
- The overview of the deep learning integrated into the medical imaging of liver: a review.Hepatology international · 2021Review
- Reliable Deep Learning-Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm Development and Validation.JMIR medical informatics · 2021Article
- A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN.Sensors (Basel, Switzerland) · 2021Article
- CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors.Sensors (Basel, Switzerland) · 2020Article
- Deep ensemble learning for Alzheimer's disease classification.Journal of biomedical informatics · 2020Article
- DE-CNN: An Improved Identity Recognition Algorithm Based on the Emotional Electroencephalography.Computational and mathematical methods in medicine · 2020Article
- EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model.Brain sciences · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this paper, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state-of-the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications. We covered various applications of electroencephalography in medicine, including emotion recognition, sleep stage classification, and seizure detection, in order to understand how deep learning algorithms could be modified to better suit the tasks desired. This review is intended to provide researchers with a broad overview of the currently existing deep belief network methodology for electroencephalography signals, as well as to highlight potential challenges for future research.
Indexed as
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What OpenQuestion holds
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.