A review on transfer learning in EEG signal analysis

Zitong Wan, Rui Yang*, Mengjie Huang*, Nianyin Zeng, Xiaohui Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

307 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalNeurocomputing
Volume421
Issue number0925-2312
DOIs
Publication statusPublished - 15 Jan 2021

Keywords

  • Electroencephalogram
  • Transfer learning

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