Term of Award
Spring 2017
Degree Name
Master of Science in Applied Engineering (M.S.A.E.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Mechanical Engineering
Committee Chair
Biswanath Samanta
Committee Member 1
Minchul Shin
Committee Member 2
JungHun Choi
Abstract
In motor imagery (MI) based brain-computer interface (BCI), success depends on reliable processing of the noisy, non-linear, and non-stationary brain activity signals for extraction of features and effective classification of MI activity as well as translation to the corresponding intended actions. In this study, signal processing and classification techniques are presented for electroencephalogram (EEG) signals for motor imagery based brain-computer interface. EEG signals have been acquired placing the electrodes following the international 10-20 system. The acquired signals have been pre-processed removing artifacts using empirical mode decomposition (EMD) and two extended versions of EMD, ensemble empirical mode decomposition (EEMD), and multivariate empirical mode decomposition (MEMD) leading to better signal to noise ratio (SNR) and reduced mean square error (MSE) compared to independent component analysis (ICA). EEG signals have been decomposed into independent mode function (IMFs) that are further processed to extract features like sample entropy (SampEn) and band power (BP). The extracted features have been used in support vector machines to characterize and identify MI activities. EMD and its variants, EEMD, MEMD have been compared with common spatial pattern (CSP) for different MI activities. SNR values from EMD, EEMD and MEMD (4.3, 7.64, 10.62) are much better than ICA (2.1) but accuracy of MI activity identification is slightly better for ICA than EMD using BP and SampEn. Further work is outlined to include more features with larger database for better classification accuracy.
OCLC Number
1001573114
Catalog Permalink
https://galileo-georgiasouthern.primo.exlibrisgroup.com/permalink/01GALI_GASOUTH/1r4bu70/alma9916057888102950
Recommended Citation
Alam, Md Erfanul, "Electroencephalographic Signal Processing and Classification Techniques for Noninvasive Motor Imagery Based Brain Computer Interface" (2017). Electronic Theses and Dissertations. 1591.
https://digitalcommons.georgiasouthern.edu/etd/1591
Research Data and Supplementary Material
No
