NDLI: Motor imagery BCI classification based on novel two-dimensional modelling in empirical wavelet transform

Content Provider	IET Digital Library
Author	Sadiq, Muhammad Tariq Yu, Xiaojun Yuan, Zhaohui Aziz, Muhammad Zulkifal
Abstract	Brain complexity and non-stationary nature of electroencephalography (EEG) signal make considerable challenges for the accurate identification of different motor-imagery (MI) tasks in brain–computer interface (BCI). In the proposed Letter, a novel framework is proposed for the automated accurate classification of MI tasks. First, raw EEG signals are denoised with multiscale principal component analysis. Secondly, denoised signals are decomposed by empirical wavelet transform into different modes. Thirdly, the two-dimensional (2D) modelling of modes is introduced to identify the variations of different signals. Fourthly, a single geometrical feature name as, a summation of distance from each point relative to a coordinate centre is extracted from 2D modelling of modes. Finally, the extracted feature vectors are provided to the feedforward neural network and cascade forward neural networks for classification check. The proposed study achieved 95.3% of total classification accuracy with 100% outcome for subject with very small training samples, which is outperforming existing methods on the same database.
Starting Page	1367
Ending Page	1369
Page Count	3
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 25, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/25
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2020.2509
Journal	Electronics Letters
Publisher Date	2020-10-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automated Accurate Classification Bioelectric Signal Biology And Medical Computing Brain Complexity Brain-computer Interfaces Brain–computer Interface Cascade Forward Neural Network Classification Check Computer Vision And Image Processing Technique Considerable Challenges Denoised Signal Different Motor-imagery Tasks Digital Signal Processing Electrical Activity in Neurophysiological Processes Electrodiagnostics And Other Electrical Measurement Technique Electroencephalography Electroencephalography Signal Empirical Wavelet Extracted Feature Vector Feature Extraction Feedforward Neural Network Integral Transforms Medical Signal Processing MI Task Motor Imagery BCI Classification Multiscale Principal Component Analysis Neural Computing Technique Neural Nets Nonstationary Nature Principal Component Analysis Probability Theory Raw EEG Signal Signal Classification Signal Denoising Signal Processing And Detection Single Geometrical Feature Name Statistics Stochastic Linearised SCUC Total Classification Accuracy User InterFace Wavelet Transform
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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