Parvez, Mohammad ZavidHossain, NahidHasan, Bhuiyan ItmamMohona, Mahfuza HumayraNoshin, Kantat Rehnuma2019-10-142019-10-1420192019-09ID 14201027ID 14201035ID 14301028ID 15301066http://hdl.handle.net/10361/12783Cataloged from PDF version of thesis.Includes bibliographical references (pages 27-31).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.Motor imagery tasks are mental processes by which individual practices a set of actions in their mind without actually performing the physical movements. Research in the motor imagery tasks allow us to acquire critical information on how the human brain works, which further enables us to integrate the knowledge with brain-computer interface (BCI) technologies to improve neurological rehabilitation along with, commercial uses such as communication, entertainment, etc. Electroencephalogram (EEG) is a commonly used process to observe and classify brain activities. However, EEG signal is non-stationary in nature, therefore, feature extraction based on EEG signals is quite hard. In our thesis, empirical mode decomposition (EMD) was used to break down the original signal into intrinsic mode functions (IMFs) in order of higher frequency to lower frequency. Convolution neural network (CNN) is then used on IMFs' feature vector and classify di erent motor imagery tasks. Our proposed model achieves around 78% accuracy, where the dataset was captured from nine participants.31 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.EEGEMDIMFCNNBCIBrain-computer interfacesHuman-computer interactionComputational intelligenceThesis