ReviewSensors (Basel, Switzerland)2020
Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.
Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Dataset Fragmentation, Cognitive Variability, and Reproducibility Challenges in EEG-Based Brain-Computer Interfaces: A PRISMA-Based Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions.Frontiers in human neuroscience · 2024Pooled it
- MDEFusion: A Multi-Domain EEG Feature Fusion Network With Bidirectional Attention LSTM and Half-Split Crossover SAE for Schizophrenia Recognition.Brain and behavior · 2026Article
- Deep learning for autism detection using clinical notes: A comparison of transfer learning for a transparent and black-box approach.Artificial intelligence in medicine · 2026Article
- Using machine learning to simultaneously quantify multiple cognitive components of episodic memory.Nature communications · 2025Article
- Transfer learning for subject-independent motor imagery EEG classification using convolutional relational networks.Frontiers in neuroscience · 2025Article
- Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data.Scientific reports · 2024Article
- Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.Sensors (Basel, Switzerland) · 2024Article
- Cross-domain prediction approach of human lower limb voluntary movement intention for exoskeleton robot based on EEG signals.Frontiers in bioengineering and biotechnology · 2024Article
- A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces.Scientific data · 2023Article
- An evaluation of transfer learning models in EEG-based authentication.Brain informatics · 2023Article
- Transfer Learning in Trajectory Decoding: Sensor or Source Space?Sensors (Basel, Switzerland) · 2023Article
- Brain-machine interface based on transfer-learning for detecting the appearance of obstacles during exoskeleton-assisted walking.Frontiers in neuroscience · 2023Article
- Survey on the research direction of EEG-based signal processing.Frontiers in neuroscience · 2023Review
- Identifying Thematics in a Brain-Computer Interface Research.Computational intelligence and neuroscience · 2023Review
- Evaluating Deep Learning Performance for P300 Neural Signal Classification.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- EEG Signal Processing for Biomedical Applications.Sensors (Basel, Switzerland) · 2022Article
- A Review of Brain Activity and EEG-Based Brain-Computer Interfaces for Rehabilitation Application.Bioengineering (Basel, Switzerland) · 2022Review
- Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey.Healthcare (Basel, Switzerland) · 2022Review
- A Two-Branch CNN Fusing Temporal and Frequency Features for Motor Imagery EEG Decoding.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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7 authors.
Funding
Abstract
The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the same probability distribution. However, this may be violated in EEG processing. Variations across sessions/subjects result in a deviation of the feature distribution of EEG signals in the same task, which reduces the accuracy of the decoding model for mental tasks. Recently, transfer learning (TL) has shown great potential in processing EEG signals across sessions/subjects. In this work, we reviewed 80 related published studies from 2010 to 2020 about TL application for EEG decoding. Herein, we report what kind of TL methods have been used (e.g., instance knowledge, feature representation knowledge, and model parameter knowledge), describe which types of EEG paradigms have been analyzed, and summarize the datasets that have been used to evaluate performance. Moreover, we discuss the state-of-the-art and future development of TL for EEG decoding. The results show that TL can significantly improve the performance of decoding models across subjects/sessions and can reduce the calibration time of brain-computer interface (BCI) systems. This review summarizes the current practical suggestions and performance outcomes in the hope that it will provide guidance and help for EEG research in the future.
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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.