A review on transfer learning in EEG signal analysis
By Zitong Wan, Rui Yang, Mengjie Huang, Nianyin Zeng, Xiaohui Liu. Neurocomputing, 2021
432 citations recorded by the source corpus.
Abstract Electroencephalogram (EEG) signal analysis, which is widely used for human-computer interaction and neurological disease diagnosis, requires a large amount of labeled data for training. However, the collection of substantial EEG data could be difficult owing to its randomness and non-stationary. Moreover, there is notable individual difference in EEG data, which affects the reusability and generalization of models. For mitigating the adverse effects from the above factors, transfer learning is applied in this field to transfer the knowledge learnt in one domain into a different but related domain. Transfer learning adjusts models with small-scale data of the task, and also maintains the learning ability with individual difference. This paper describes four main methods of transfer learning and explores their practical applications in EEG signal analysis in recent years. Finally, we discuss challenges and opportunities of transfer learning and suggest areas for further study.
Subfields: Cognitive Neuroscience, Human-Computer Interaction, Signal Processing
View Paper: https://www.semanticscholar.org/paper/6910367849e56f40ba55ee26d3d95263f6bb1ee9
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Citations in the corpus
- Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review (2021, methodology): “Using deep network adaptation is another possible solution [40].”
- Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, background): “Transfer learning, introduced in 1995 to deal with life-long learning problems by reusing previous knowledge, relaxes the restriction that training and test data must follow the same distribution [46, 48].”