ReviewSensors (Basel, Switzerland)2025
Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Frequency-aware transformer networks for robust and generalizable EEG-based seizure detection.Scientific reports · 2026Article
- Leveraging pretrained vision transformers for classifying alcohol use disorder using raw resting-state EEG.Neuroimage. Reports · 2026Article
- Power and Phase Fusion Spectrogram with Three-Dimensional Convolution and Vision Transformer for Seizure Detection.Diagnostics (Basel, Switzerland) · 2026Article
- EEG-Controlled Exoskeleton for Walking and Standing: A Longitudinal Multimodal Dataset of Healthy Individuals.Scientific data · 2026Article
- Sensor-Based Fault Diagnosis and Prognosis of Neurophysiological States: A Transformer Autoencoder Approach to EEG Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Maximizing Single-Feature Separability for Improving Transfer Learning in Motor Imagery EEG Decoding.Brain sciences · 2026Article
- Deception Detection from Five-Channel Wearable EEG on LieWaves: A Reproducible Baseline for Subject-Dependent and Subject-Independent Evaluation.Sensors (Basel, Switzerland) · 2026Article
- Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs.Scientific reports · 2026Article
- Leveraging Pretrained Vision Transformers for classifying Alcohol Use Disorder using Raw Resting-State EEG.bioRxiv : the preprint server for biology · 2026Article
- EEG Signal Classification with Data Augmentation for Epileptic Focus Localization and Deep Sleep Detection.Sensors (Basel, Switzerland) · 2026Article
- EEG-based cognition-aware task classification and scheduling using enhanced fuzzy transition modeling.Frontiers in artificial intelligence · 2026Article
- MSGM: a multi-scale spatiotemporal graph Mamba for EEG emotion recognition.Frontiers in neuroscience · 2026Article
- Hybrid lightweight vision transformers with attention mechanism for feature extraction and classification of product designs.PloS one · 2026Article
- Localizing the epileptogenic zone in drug-resistant epilepsy based on ictal SEEG high-resolution time-frequency representation.Frontiers in neuroscience · 2026Article
- Research on Driver Fatigue Detection in Real Driving Environments Based on Semi-Dry Electrodes with Automatic Conductive Fluid Replenishment.Sensors (Basel, Switzerland) · 2025Article
- POC Sensor Systems and Artificial Intelligence-Where We Are Now and Where We Are Going?Biosensors · 2025Review
- A cross-domain framework for emotion and stress detection using WESAD, SCIENTISST-MOVE, and DREAMER datasets.Frontiers in bioengineering and biotechnology · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transformers have rapidly influenced research across various domains. With their superior capability to encode long sequences, they have demonstrated exceptional performance, outperforming existing machine learning methods. There has been a rapid increase in the development of transformer-based models for EEG analysis. The high volumes of recently published papers highlight the need for further studies exploring transformer architectures, key components, and models employed particularly in EEG studies. This paper aims to explore four major transformer architectures: Time Series Transformer, Vision Transformer, Graph Attention Transformer, and hybrid models, along with their variants in recent EEG analysis. We categorize transformer-based EEG studies according to the most frequent applications in motor imagery classification, emotion recognition, and seizure detection. This paper also highlights the challenges of applying transformers to EEG datasets and reviews data augmentation and transfer learning as potential solutions explored in recent years. Finally, we provide a summarized comparison of the most recent reported results. We hope this paper serves as a roadmap for researchers interested in employing transformer architectures in EEG analysis.
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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.