ReviewBrain sciences2021
Neural Decoding of EEG Signals with Machine Learning: A Systematic Review.
Review in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 4 of them syntheses 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
63 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Electroencephalography in Subjective Cognitive Decline and Mild Cognitive Impairment: Systematic Review of Biomarkers, Classification, and Prognostic Evidence.Journal of medical Internet research · 2026Pooled it
- Electroencephalography in motor decision-making: a scoping review in athletes.Frontiers in psychology · 2026Pooled it
- Integrating Neuroimaging and Machine Learning to Predict Mental Disorder Outcomes: A Systematic Review.Advances in experimental medicine and biology · 2026Pooled it
- Application of machine learning approaches to predict seizure-onset zones in patients with drug-resistant epilepsy: a systematic review.Frontiers in neurology · 2025Pooled it
- NeuroFusion: A Unified Framework for Generalized Visual Stimulus Decoding from fMRI Across Datasets and Subjects.Neuroinformatics · 2026Article
- Effect of Emotional States on EEG-Based Biometric Identification: A Comparative Study of Classifiers.Bioengineering (Basel, Switzerland) · 2026Article
- Quantitative Electroencephalography as a Complement to Symptom-Based Psychiatric Diagnosis: A Narrative Review.Journal of personalized medicine · 2026Review
- Machine-learning methods for epilepsy diagnosis and therapeutic prevention: advances, setbacks, and opportunities.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- Automating Critical Care EEG: Enhancing Accuracy, Efficiency, and Patient Outcomes in Critical Care.Epilepsy currents · 2026Article
- Bridging Neural Topology and Affective Computing: Graph Attention for EEG Emotion Recognition.Journal of medical systems · 2026Review
- A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems.IEEE transactions on bio-medical engineering · 2026Article
- Hybrid deep learning for mental workload classification using EEG with enhanced preprocessing and interpretability.PloS one · 2026Article
- Quantitative electroencephalography as a next-generation tool in neurodiagnostics: significance, clinical applications, and practical interpretative frameworks.Frontiers in neuroscience · 2026Review
- ThinkSTra: a transformer-driven architecture for decoding imagined speech from EEG with spatial-temporal dynamics.Medical & biological engineering & computing · 2025Article
- A large electroencephalogram database of freewill reaching and grasping tasks for brain machine interfaces.Scientific data · 2025Article
- MS-CANet: lightweight multi-scale channel attention network with depthwise residual blocks for EEG-based spatial cognition evaluation.Medical & biological engineering & computing · 2025Article
- Development and validation of a multimodal automatic interictal epileptiform discharge detection model: a prospective multi-center study.BMC medicine · 2025Article
- Hybrid EEG Feature Learning Method for Cross-Session Human Mental Attention State Classification.Brain sciences · 2025Article
- BCINetV1: Integrating Temporal and Spectral Focus Through a Novel Convolutional Attention Architecture for MI EEG Decoding.Sensors (Basel, Switzerland) · 2025Article
- How EEG preprocessing shapes decoding performance.Communications biology · 2025Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Electroencephalography (EEG) is a non-invasive technique used to record the brain's evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.
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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.