Classification of Motor Imagery EEG Signal for Navigation of Brain Controlled Drones


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Maiti, S and Mandal, AS and Chaudhury, S (2019) Classification of Motor Imagery EEG Signal for Navigation of Brain Controlled Drones. In: 11th International Conference Intelligent Human Computer Interaction (IHCI-2019), December 12-14, 2019, Allahabad, India.

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Navigation of drones can be conceivably performed by operators by analyzing the brain signals of the person. EEG signal corresponding to the motor imaginations can be used for generation of control signals for drone. Different machine learning and deep learning approaches have been developed in the state of the art literature for the classification of motor imagery EEG signal. There is still a need for developing a suitable model that can classify the motor imagery signal fast and can generate a navigation command for drone in real-time. In this paper, we have reported the performance of convolutional stacked autoencoder and Convolutional Long short term memory models for classification of Motor imagery EEG signal. The developed models have been optimized using TensorRT that speeds up inference performance and the inference engine has been deployed on Jetson TX2 embedded platform. The performance of these models have been compared with different machine learning models.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Motor Imagery. Long Short Term Memory. Convolutional Stacked Autoencoder. Drone. Jetson TX2.
Subjects: Electronic Systems > Digital Systems
Divisions: Electronic Systems
Depositing User: Mr. Jitendra Nath Bajpai
Date Deposited: 10 Sep 2021 11:29
Last Modified: 10 Sep 2021 11:29

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