Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159504
Title: A new framework for automatic detection of motor and mental imagery EEG signals for robust BCI systems
Authors: Yu, Xiaojun
Muhammad Zulkifal Aziz
Muhammad Tariq Sadiq
Fan, Zeming
Xiao, Gaoxi
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Yu, X., Muhammad Zulkifal Aziz, Muhammad Tariq Sadiq, Fan, Z. & Xiao, G. (2021). A new framework for automatic detection of motor and mental imagery EEG signals for robust BCI systems. IEEE Transactions On Instrumentation and Measurement, 70, 1006612-. https://dx.doi.org/10.1109/TIM.2021.3069026
Journal: IEEE Transactions on Instrumentation and Measurement
Abstract: Nonstationary signal decomposition (SD) is a primary procedure to extract monotonic components or modes from electroencephalogram (EEG) signals for the development of robust brain-computer interface (BCI) systems. This study proposes a novel automated computerized framework for proficient identification of motor and mental imagery (MeI) EEG tasks by employing empirical Fourier decomposition (EFD) and improved EFD (IEFD) methods. Specifically, the multiscale principal component analysis (MSPCA) is rendered to denoise EEG data first, and then, EFD is utilized to decompose nonstationary EEG into subsequent modes, while the IEFD criterion is proposed for a single conspicuous mode selection. Finally, the time-and frequency-domain features are extracted and classified with a feedforward neural network (FFNN) classifier. Extensive experiments are conducted on four multichannel motor and MeI data sets from BCI competitions II and III using a tenfold cross-validation strategy. Results compared with the other existing methods demonstrated that the highest classification accuracies of 99.82% (data set IV-A), 93.33% (data set IV-b), 91.96% (data set III), and 88.08% (data set V) in subject-specific scenarios, while 82.70% (data set IV-A) in the subject-independent framework are achieved for IEFD with FFNN classifiers collectively. The overall exploratory results authenticate that the proposed IEFD-based automated computerized framework not only outperforms the conventional SD methods but is also robust and computationally efficient for the development of subject-dependent and subject-independent BCI systems.
URI: https://hdl.handle.net/10356/159504
ISSN: 0018-9456
DOI: 10.1109/TIM.2021.3069026
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

SCOPUSTM   
Citations 10

59
Updated on Jun 20, 2024

Web of ScienceTM
Citations 20

6
Updated on Oct 27, 2023

Page view(s)

87
Updated on Jun 22, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.