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In this paper, we focus on estimating Direction of Arrival (DOA) and removing heavy clutter embedded with measurement noise. A correlated Gaussian process is chosen to model destructive effects of clutter. Also, a white Gaussian process is selected to describe measurement noise caused by sensor array. After adding these distortions to the off-grid model, we utilize Sparse Bayesian Learning and principal component analysis (as a preprocessing stage) in order to remove these distortions as well as estimating of true DOAs. Finally, at the end we will show how ignorance of clutter from model or combine it with measurement noise degrade DOA estimation. This will be demonstrated by various numerical simulations.

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The most important challenges of classifying Motor Imagery tasks based on the EEG signal are low signal-to-noise ratio, non-stationarity, and the high subject dependence of the EEG signal. In this study, a framework for multi-class decoding of Motor Imagery signals is presented. This framework is based on information theory and hybrid deep learning along with transfer learning. In this study, the OVR-FBDiv method, which is based on the symmetric Kullback-Leibler divergence, is used to differentiate between features of different classes and highlight them. Then, the mRMR algorithm is used to select the most distinctive features obtained from the filters of symmetric KL divergence. Finally, a hybrid deep neural network consisting of CNN and LSTM is used to learn the spatial and temporal features of the EEG signal along with the transfer learning technique to overcome the problem of subject dependence in EEG signals. The average value of Kappa for the classification of 4-class Motor Imagery data on BCI competition IV dataset 2a by the proposed method is 0.84. Also, the proposed method is compared with other state-of-the-art methods.