Empirical Mode Decomposition Coupled with Fast Fourier Transform based Feature Extraction Method for Motor Imagery Tasks Classification
Brain-Computer Interfaces (BCI) offers a robust solution to the people with disabilities and allows for creative connectivity between the user's intention and supporting tools. Different signals from the human brain, including the motor imagery, steady-state visual evoked potential, error-r...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE Malaysia Section Control Systems Chapter (CSS)
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30628/ http://umpir.ump.edu.my/id/eprint/30628/7/Empirical%20Mode%20Decomposition%20Coupled%20with%20Fast1.pdf |
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| author | Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Bari, Bifta Sama Mahfuzah, Mustafa Mohd Shawal, Jadin |
| author_facet | Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Bari, Bifta Sama Mahfuzah, Mustafa Mohd Shawal, Jadin |
| author_sort | Islam, Md Nahidul |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Brain-Computer Interfaces (BCI) offers a robust
solution to the people with disabilities and allows for creative
connectivity between the user's intention and supporting tools.
Different signals from the human brain, including the motor
imagery, steady-state visual evoked potential, error-related
potential (ErrP), motion-related potentials and P300 have been
employed to design a competent BCI system. Motor imagery is
commonly seen in almost every BCI system among these neural
signals. This article has implemented feature extraction and
feature selection techniques to classify the
Electrocorticography (ECoG) motor imaging signal. The
empirical mode decomposition (EMD) coupled fast Fourier
transform (FFT) has been utilized as the feature extraction and
recursive feature elimination (RFE) has been utilised to select
the features. Finally, the extracted features have been classified
using K-nearest neighbor, support vector machine and linear
discriminant analysis. Two classes ECoG data from dataset I
(BCI competition III) have been considered to validate the
proposed method. In contrast with other state of the art
techniques that employed the same dataset, the presented
feature extraction and selection method significantly improve
the classification accuracy (maximum achieved accuracy was
95.89% with SVM).
Keywords—Electrocorticography (ECoG), Empirical Mode
Decomposition (EMD), Brain Computer Interfaces (BCI),
Machine Learning, Motor Imagery |
| first_indexed | 2025-11-15T02:59:05Z |
| format | Conference or Workshop Item |
| id | ump-30628 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:59:05Z |
| publishDate | 2020 |
| publisher | IEEE Malaysia Section Control Systems Chapter (CSS) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-306282021-02-05T09:05:32Z http://umpir.ump.edu.my/id/eprint/30628/ Empirical Mode Decomposition Coupled with Fast Fourier Transform based Feature Extraction Method for Motor Imagery Tasks Classification Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Bari, Bifta Sama Mahfuzah, Mustafa Mohd Shawal, Jadin TK Electrical engineering. Electronics Nuclear engineering Brain-Computer Interfaces (BCI) offers a robust solution to the people with disabilities and allows for creative connectivity between the user's intention and supporting tools. Different signals from the human brain, including the motor imagery, steady-state visual evoked potential, error-related potential (ErrP), motion-related potentials and P300 have been employed to design a competent BCI system. Motor imagery is commonly seen in almost every BCI system among these neural signals. This article has implemented feature extraction and feature selection techniques to classify the Electrocorticography (ECoG) motor imaging signal. The empirical mode decomposition (EMD) coupled fast Fourier transform (FFT) has been utilized as the feature extraction and recursive feature elimination (RFE) has been utilised to select the features. Finally, the extracted features have been classified using K-nearest neighbor, support vector machine and linear discriminant analysis. Two classes ECoG data from dataset I (BCI competition III) have been considered to validate the proposed method. In contrast with other state of the art techniques that employed the same dataset, the presented feature extraction and selection method significantly improve the classification accuracy (maximum achieved accuracy was 95.89% with SVM). Keywords—Electrocorticography (ECoG), Empirical Mode Decomposition (EMD), Brain Computer Interfaces (BCI), Machine Learning, Motor Imagery IEEE Malaysia Section Control Systems Chapter (CSS) 2020-12-01 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30628/7/Empirical%20Mode%20Decomposition%20Coupled%20with%20Fast1.pdf Islam, Md Nahidul and Norizam, Sulaiman and Rashid, Mamunur and Bari, Bifta Sama and Mahfuzah, Mustafa and Mohd Shawal, Jadin (2020) Empirical Mode Decomposition Coupled with Fast Fourier Transform based Feature Extraction Method for Motor Imagery Tasks Classification. In: 2020 IEEE International Conference on System Engineering and Technology (ICSET) , 9 November 2020 , UiTM Shah Alam, Selangor (Virtual Conference). pp. 256-261. (20199954). ISBN 978-1-7281-9910-8 (Published) http://10.1109/ICSET51301.2020.9265370 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Bari, Bifta Sama Mahfuzah, Mustafa Mohd Shawal, Jadin Empirical Mode Decomposition Coupled with Fast Fourier Transform based Feature Extraction Method for Motor Imagery Tasks Classification |
| title | Empirical Mode Decomposition Coupled with Fast Fourier Transform based Feature Extraction Method for Motor Imagery Tasks Classification |
| title_full | Empirical Mode Decomposition Coupled with Fast Fourier Transform based Feature Extraction Method for Motor Imagery Tasks Classification |
| title_fullStr | Empirical Mode Decomposition Coupled with Fast Fourier Transform based Feature Extraction Method for Motor Imagery Tasks Classification |
| title_full_unstemmed | Empirical Mode Decomposition Coupled with Fast Fourier Transform based Feature Extraction Method for Motor Imagery Tasks Classification |
| title_short | Empirical Mode Decomposition Coupled with Fast Fourier Transform based Feature Extraction Method for Motor Imagery Tasks Classification |
| title_sort | empirical mode decomposition coupled with fast fourier transform based feature extraction method for motor imagery tasks classification |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/30628/ http://umpir.ump.edu.my/id/eprint/30628/ http://umpir.ump.edu.my/id/eprint/30628/7/Empirical%20Mode%20Decomposition%20Coupled%20with%20Fast1.pdf |