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Classifying different movement of human body based on EEG data using Machine Learning Algorithms.

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dc.contributor.author Halder, Antony Polock
dc.contributor.author Rana, Md. Sohel
dc.contributor.author Hasan, Md. Mubashir
dc.date.accessioned 2019-03-28T06:31:55Z
dc.date.available 2019-03-28T06:31:55Z
dc.date.issued 2018-12-22
dc.identifier.uri http://dspace.ewubd.edu/handle/2525/3025
dc.description This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh en_US
dc.description.abstract In this paper, we propose an automated computer platform for the purpose of classifying Electroencephalography (EEG) signals associated with left and right hand movements using a hybrid system that uses advanced feature extraction techniques and machine learning algorithms. It is known that EEG represents the brain activity by the electrical voltage fluctuations along the scalp, and Brain-Computer Interface (BCI) is a device that enables the use of the brain‘s neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements. In our research work, we aspired to find the best feature extraction method that enables the differentiation between left and right executed fist movements through various classification algorithms. The EEG dataset used in this research was created and contributed to PhysioNet by the developers of the BCI2000 instrumentation system. Data was preprocessed using the EEGLAB MATLAB toolbox and artifacts removal was done using AAR. Data was epoched on the basis of Event-Related (De) Synchronization (ERD/ERS) and movement-related cortical potentials (MRCP) features. Mu/beta rhythms were isolated for the ERD/ERS analysis and delta rhythms were isolated for the MRCP analysis. The Independent Component Analysis (ICA) spatial filter was applied on related channels for noise reduction and isolation of both artifactually and neutrally generated EEG sources. The final feature vector included the ERD, ERS, and MRCP features in addition to the mean, power and energy of the activations of the resulting Independent Components (ICs) of the epoched feature datasets. The datasets were inputted into two machine- learning algorithms: NFL, Fuzzy Logic and Support Vector Machines (SVMs). Intensive experiments were carried out and optimum classification performances of obtained using NFL, Fuzzy Logic and SVM, respectively. This research shows that this method of feature extraction holds some promise for the classification of various pairs of motor movements, which can be used in a BCI context to mentally control a computer or machine. en_US
dc.language.iso en_US en_US
dc.publisher East West University en_US
dc.relation.ispartofseries ;ECE00178
dc.subject Human body based on EEG data using Machine Learning Algorithms en_US
dc.title Classifying different movement of human body based on EEG data using Machine Learning Algorithms. en_US
dc.type Thesis en_US


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