ArticleFrontiers in neuroscience2023
EEGformer: A transformer-based brain activity classification method using EEG signal.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Cerebro Wave Bee transformer to leverage EEG based hand movement classification using optimized bio-inspired learning rates.Scientific reports · 2026Article
- Automating Critical Care EEG: Enhancing Accuracy, Efficiency, and Patient Outcomes in Critical Care.Epilepsy currents · 2026Article
- EEG-TriNet++: A Transformer-Guided Meta-Learning Framework for Robust and Generalizable Motor Imagery Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Analysis of the generalization ability of graph neural networks in cross-subject EEG emotion recognition.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Artificial intelligence-based analysis of visual electrophysiological signals for clinical interpretation support.Frontiers in neuroscience · 2026Review
- Leednet: a lightweight network for event detection in EEG signals.Journal on advances in signal processing · 2026Article
- LPGGNet: Learning from Local-Partition-Global Graph Representations for Motor Imagery EEG Recognition.Brain sciences · 2025Article
- Harnessing Trial-to-Trial Variability of EEG Spectral Characteristics to Understand Autism.Journal of autism and developmental disorders · 2025Article
- Enhanced SSVEP Bionic Spelling via xLSTM-Based Deep Learning with Spatial Attention and Filter Bank Techniques.Biomimetics (Basel, Switzerland) · 2025Article
- Annotating neurophysiologic data at scale with optimized human input.Journal of neural engineering · 2025Article
- Self-Supervised Learning with Adaptive Frequency-Time Attention Transformer for Seizure Prediction and Classification.Brain sciences · 2025Article
- Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification.Sensors (Basel, Switzerland) · 2025Review
- Augmenting Electroencephalogram Transformer for Steady-State Visually Evoked Potential-Based Brain-Computer Interfaces.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- Attention-based Transformer-LSTM architecture for early diagnosis and staging of early-stage Parkinson's disease using fNIRS data.Frontiers in aging neuroscience · 2025Article
- A Bibliometric Analysis of the Application of Brain-Computer Interface in Rehabilitation Medicine Over the Past 20 Years.Journal of multidisciplinary healthcare · 2025Article
- Tackling the possibility of extracting a brain digital fingerprint based on personal hobbies predilection.Frontiers in neuroscience · 2025Article
- Improving diagnostic accuracy of routine EEG for epilepsy using deep learning.Brain communications · 2025Article
- Deep learning approaches for EEG-based healthcare applications: a comprehensive review.Frontiers in human neuroscience · 2025Review
- Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data.Journal of neuroscience methods · 2024Article
- EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts.Scientific data · 2024Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: The effective analysis methods for steady-state visual evoked potential (SSVEP) signals are critical in supporting an early diagnosis of glaucoma. Most efforts focused on adopting existing techniques to the SSVEPs-based brain-computer interface (BCI) task rather than proposing new ones specifically suited to the domain. Method: Given that electroencephalogram (EEG) signals possess temporal, regional, and synchronous characteristics of brain activity, we proposed a transformer-based EEG analysis model known as EEGformer to capture the EEG characteristics in a unified manner. We adopted a one-dimensional convolution neural network (1DCNN) to automatically extract EEG-channel-wise features. The output was fed into the EEGformer, which is sequentially constructed using three components: regional, synchronous, and temporal transformers. In addition to using a large benchmark database (BETA) toward SSVEP-BCI application to validate model performance, we compared the EEGformer to current state-of-the-art deep learning models using two EEG datasets, which are obtained from our previous study: SJTU emotion EEG dataset (SEED) and a depressive EEG database (DepEEG). Results: The experimental results show that the EEGformer achieves the best classification performance across the three EEG datasets, indicating that the rationality of our model architecture and learning EEG characteristics in a unified manner can improve model classification performance. Conclusion: EEGformer generalizes well to different EEG datasets, demonstrating our approach can be potentially suitable for providing accurate brain activity classification and being used in different application scenarios, such as SSVEP-based early glaucoma diagnosis, emotion recognition and depression discrimination.
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