Transfer Learning based Motor Imagery Classification using Convolutional Neural Networks

Milad Parvan, Amir Rikhtehgar Ghiasi, Tohid Yousefi Rezaii, Ali Farzamnia

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

24 Citations (Scopus)


Nowadays, classification of signals is considered as the crucial role of motor imagery brain computer interface. Moreover, deep learning approaches show acceptable performance in image recognition applications as well as speech recognition. However, practicality of the aforementioned technique is not generally deployed on motor imagery tasks. Hence, the goal of this paper is to apply convolutional neural networks to classify the motor imagery EEG signals. In addition, data augmentation along with excusive transfer learning strategy are used to overcome the problem of few trials in motor imagery tasks. On the other hand, analytical regression assessments are also applied to the raw data for mitigating the stress of EOG on EEG. Consequently, the simulation results clearly convey the contribution of the proposed algorithm via testing on BCI competition IV dataset 2b. Applying EOG artifact removal and data augmentation methods resulted in 0.07 improvement in kappa coefficient. Furthermore, using our proposed transfer learning method led to 0.06 improvement in terms of kappa coefficient.

Original languageEnglish
Title of host publicationICEE 2019 - 27th Iranian Conference on Electrical Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781728115085, 9781728115078
ISBN (Print)9781728115092
Publication statusPublished - 5 Aug 2019
Externally publishedYes
Event27th Iranian Conference on Electrical Engineering - Yazd, Iran, Islamic Republic of
Duration: 30 Apr 20192 May 2019
Conference number: 27

Publication series

NameIranian Conference on Electrical Engineering
ISSN (Print)2164-7054
ISSN (Electronic)2642-9527


Conference27th Iranian Conference on Electrical Engineering
Abbreviated titleICEE 2019
Country/TerritoryIran, Islamic Republic of

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