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Motor Imagery Recognition of EEG Signal using Cuckoo-Search Masking Empirical Mode Decomposition

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Motor Imagery Recognition of EEG Signal using Cuckoo-Search Masking Empirical Mode Decomposition


Jaipriya D



Jaipriya D "Motor Imagery Recognition of EEG Signal using Cuckoo-Search Masking Empirical Mode Decomposition" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2, February 2020, pp.491-494, URL: https://www.ijtsrd.com/papers/ijtsrd30020.pdf

Brain Computer Interface (BCI) aims at providing an alternate means of communication and control to people with severe cognitive or sensory-motor disabilities. Brain Computer Interface in electroencephalogram (EEG) is of great important but it is challenging to manage the non-stationary EEG. EEG signals are more vulnerable to contamination due to noise and artifacts. In our proposed work, we used Cuckoo-Search Masking Empirical Mode decomposition to ignore such vulnerable things. Initially, the features of EEG signals are taken such as Energy, AR Coefficients, Morphological features and Fuzzy Approximate Entropy. Then, for Feature extraction method, Masking Empirical Mode Decomposition (MEMD) is applied to deal with motor imagery (MI) recognition tasks. The EEG signal is decomposed by MEMD and hybrid features are then extracted from the first two intrinsic mode functions (IMFs). After the extracted features, Cuckoo Search algorithm is used to select the significant features. Different weights for the relevance and redundancy in the fitness function of the proposed algorithm are used to further improve their performance in terms of the number of features and the classification accuracy and finally they are fed into Linear Discriminant Analysis for classification. This analysis produces models whose accuracy is as good as more complex method. The results show that our proposed method can achieve the highest accuracy, maximal MI, recall as well as precision for Motor Imagery Recognition tasks. Our proposed method is comparable or superior than existing method.

Brain Computer Interface, Motor Imagery(MI) Recognition, Empirical Mode Decomposition


IJTSRD30020
Volume-4 | Issue-2, February 2020
491-494
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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