Abstract: This disclosure relates generally to the field of Electroencephalogram (EEG) classification, and, more particularly, to method and system for EEG motor imagery classification. Existing deep learning works employ the sensor-space for EEG graph representations wherein the channels of the EEG are considered as nodes and connection between the nodes are either predefined or are based on certain heuristics. However, these representations are ineffective and fail to accurately capture the underlying brain's functional networks. Embodiments of present disclosure provide a method of training a weighted adjacency matrix and a Graph Neural Network (GNN) to accurately represent the EEG signals. The method also trains a graph, a node, and an edge classifier to perform graph classification (i.e. motor imagery classification), node and edge classification. Thus, representations generated by the GNN can be additionally used for node and edge classification unlike state of the art methods.

Abstract: A method and system for activating thrusters of a vehicle for trajectory-tracking control of the vehicle. A transfer orbit generator to generate a transfer orbit for the vehicle from an initial orbit to a target orbit, and a feedback stabilization controller. Compute the target orbit for the vehicle about the celestial body. Compute a free trajectory with patch points along the free trajectory using a free trajectory module, each patch point includes a position and a velocity. Determine a feedback gain at each patch point using a feedback gain module, wherein a state penalty function at each patch point is set to match a state uncertainty function at the same patch point. Apply the feedback gain at each patch point to map the position and the velocity at each patch point to delta v commands, to maintain the target orbit using a feedback stabilization controller.

Abstract: A method and system for activating thrusters of a vehicle for trajectory-tracking control of the vehicle. A transfer orbit generator to generate a transfer orbit for the vehicle from an initial orbit to a target orbit, and a feedback stabilization controller. Compute the target orbit for the vehicle about the celestial body. Compute a free trajectory with patch points along the free trajectory using a free trajectory module, each patch point includes a position and a velocity. Determine a feedback gain at each patch point using a feedback gain module, wherein a state penalty function at each patch point is set to match a state uncertainty function at the same patch point. Apply the feedback gain at each patch point to map the position and the velocity at each patch point to delta v commands, to maintain the target orbit using a feedback stabilization controller.