Motor imagery EEG signal classification based on deep transfer learning

Mingnan Wei, Rui Yang, Mengjie Huang*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

21 Citations (Scopus)

Abstract

Deep transfer learning (DTL) has developed rapidly in the field of motor imagery (MI) on brain-computer interface (BCI) in recent years. DTL utilizes deep neural networks with strong generalization capabilities as the pre-training framework and automatically extracts richer and more expressive features during the training process. The goal of this paper is utilizing the DTL to classify MI electroencephalogram (EEG) signals on the premise of a small data set. The publicly available dataset III of the second BCI competition is applied in both the training part and testing part to evaluate the effectiveness of the proposed method. Firstly in the process, finite impulse response (FIR) filter and wavelet transform threshold denoising method are used to remove redundant signals and artifacts in EEG signals. Then, the continuous wavelet transform (CWT) is utilized to convert the one-dimensional EEG signal into a two-dimensional time-frequency amplitude representation as the input of the pre-trained convolutional neural network (CNN) for classifying two types of MI signals. Employing the input data of 140 trials for training, the final classification accuracy rate reaches 96.43%. Compared with the results of some superior machine learning models using the same data set, the accuracy and Kappa value of this DTL model are better. Therefore, the proposed scheme of MI EEG signal classification based on the DTL method offers preferably empirical performance.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 34th International Symposium on Computer-Based Medical Systems, CBMS 2021
EditorsJoao Rafael Almeida, Alejandro Rodriguez Gonzalez, Linlin Shen, Bridget Kane, Agma Traina, Paolo Soda, Jose Luis Oliveira
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages85-90
Number of pages6
ISBN (Electronic)9781665441216
DOIs
Publication statusPublished - Jun 2021
Event34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021 - Virtual, Online
Duration: 7 Jun 20219 Jun 2021

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Volume2021-June
ISSN (Print)1063-7125

Conference

Conference34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021
CityVirtual, Online
Period7/06/219/06/21

Keywords

  • Brain-Computer Interface
  • Convolutional Neural Network
  • Deep Transfer Learning
  • Motor Imagery

Fingerprint

Dive into the research topics of 'Motor imagery EEG signal classification based on deep transfer learning'. Together they form a unique fingerprint.

Cite this