Using Convolution Neural Networks Pattern for Classification of Motor Imagery in BCI System

Sepideh Zolfaghari, Tohid Yousefi Rezaii, Saeed Meshgini, Ali Farzamnia

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

1 Citation (Scopus)


The Electroencephalography (EEG) based Brain-computer interfaces (BCI) enable humans to control external devices through extracts informative features from brain signals and convert these features into control commands. Deep learning methods have been the advanced classification algorithms used in various applications. In this paper, the informative features of EEG signals are obtained using the filter-bank common spatial pattern (FBCSP), then the selected features which are prepared using the mutual information method are fed to the classifiers as input. Convolution neural network (CNN), Naive Bayesian (NB), multiple support vector machines (SVM) and linear discriminant analysis (LDA) algorithms are used to classify EEG signals into left and right hand motor imagery (MI) across nine subjects. Our framework has been tested on BCI competition IV-2a 4-class dataset. The results are shown that the CNN classifier has yielded the best average classification accuracy, with 99.77% as compared to other classification methods. The experimental results represent that our proposed method can obtain more refined control in the BCI applications such as controlling robot arm movement.

Original languageEnglish
Title of host publicationProceedings of the 11th National Technical Seminar on Unmanned System Technology 2019
Subtitle of host publicationNUSYS 2019
EditorsZainah Md Zain, Hamzah Ahmad, Dwi Pebrianti, Mahfuzah Mustafa, Nor Rul Hasma Abdullah, Rosdiyana Samad, Maziyah Mat Noh
PublisherSpringer Singapore
Number of pages10
ISBN (Electronic)9789811552816
ISBN (Print)9789811552809, 9789811552830
Publication statusPublished - 8 Jul 2020
Externally publishedYes
Event11th National Technical Symposium on Unmanned System Technology - Kuantan, Malaysia
Duration: 2 Dec 20193 Dec 2019
Conference number: 11

Publication series

NameLecture Notes in Electrical Engineering
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


Conference11th National Technical Symposium on Unmanned System Technology
Abbreviated titleNUSYS 2019

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