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Optimized DNN Classification Framework based on Filter Bank Common Spatial Pattern (FBCSP) for Motor-imagery-based BCI

by Zayyanu Shuaibu, Li Qi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 15
Year of Publication: 2020
Authors: Zayyanu Shuaibu, Li Qi
10.5120/ijca2020920646

Zayyanu Shuaibu, Li Qi . Optimized DNN Classification Framework based on Filter Bank Common Spatial Pattern (FBCSP) for Motor-imagery-based BCI. International Journal of Computer Applications. 175, 15 ( Aug 2020), 16-25. DOI=10.5120/ijca2020920646

@article{ 10.5120/ijca2020920646,
author = { Zayyanu Shuaibu, Li Qi },
title = { Optimized DNN Classification Framework based on Filter Bank Common Spatial Pattern (FBCSP) for Motor-imagery-based BCI },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2020 },
volume = { 175 },
number = { 15 },
month = { Aug },
year = { 2020 },
issn = { 0975-8887 },
pages = { 16-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number15/31529-2020920646/ },
doi = { 10.5120/ijca2020920646 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:25:07.378931+05:30
%A Zayyanu Shuaibu
%A Li Qi
%T Optimized DNN Classification Framework based on Filter Bank Common Spatial Pattern (FBCSP) for Motor-imagery-based BCI
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 15
%P 16-25
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Brain-Computer Interface (BCI) is a kind of communication channel between the brain and the outside world that is not dependent on the nervous system. The recent improvement of deep neural network (DNN) in a classification task lead several researchers to apply DNN methods for BCI motor imagery (MI) classification. However, DNN based classification methods for MI classification either record low accuracy or take a long time to classify MI signal due to the high dimensional nature of the MI signal. This issue prevents the DNN based MI systems from being deployed in a real application. An ideal DNN classification framework for the MI classification was proposed in this study, which can be trained with better accuracy in a short period. To address the long DNN training time and the challenges of low classification accuracy, a new method was proposed that uses the FBCSP method to reduce the data dimension and increase class discrimination by extracting the best subject-specific features of the CSP from raw EEG data as input into the DNN. The DNN is designed with a few layers, and several DNN hyperparameters have been evaluated. A configuration that has performed better in terms of minimum training time and good classification accuracy has been selected. To investigate the accuracy of the proposed DNN methods called FBCSP-DNN; first, we use traditional approaches such as LDA, SVM, and KNN to create a baseline; second, we choose some DNN studies used in other studies that have been applied to the same dataset used in this study; third, we look at the use of transfer learning, a network pre-training methodology for one data collection of some subjects and then fine-tuning for another to improve some subjects with low accuracy; finally, the results obtained using the proposed were compared to traditional methods and other competing DNN methods used in other studies. The performance of the proposed FBSCP-DNN method is evaluated using BCI competition IV dataset 2a. The results show that the proposed method (81.43%) is superior to the traditional classification methods (SVM: 72.37%; KNN: 61.06%; LDA: 72.07%). We also compared the proposed DNN method and other DNN methods proposed by other studies using the same dataset. The proposed method outperforms other studies in terms of accuracy of the MI classification for within-subjects and cross-subjects, which improved by 6% and 9% respectively. The FBCSP-DNN classification framework proposed in this paper has the advantages of short training time and high classification accuracy, which ensures reliability for the practical application of BCI-motor imagery systems.

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Index Terms

Computer Science
Information Sciences

Keywords

Brain-Computer Interface (BCI) Deep Neural Network (DNN) Motor Imagery (MI) Filter Bank Common Spatial Pattern (FBCSP) electroencephalography (EEG).

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