Recognition of motor imagery tasks (MI) from electroencephalographic (EEG) signals is crucial for developing rehabilitation and motor assisted devices based on brain-computer interfaces (BCI). Here we consider the challenge of learning a classifier, based on relevant patterns of the EEG signals; this learning step typically involves both feature selection, as well as a base learning algorithm. However, in many cases it is not clear what combination of these methods will yield the best classifier. This paper contributes a detailed assessment of feature selection techniques, viz., squared Pearson's correlation (R 2 ), principal component analysis (PCA), kernel principal component analysis (kPCA) and fast correlation-based filter (FCBF); and the learning algorithms: linear discriminant analysis (LDA), support vector machines (SVM), and Feed Forward Neural Network (NN). A systematic evaluation of the combinations of these methods was performed in three two-class classification scenarios: rest vs. movement, upper vs. lower limb movement and right vs. left hand movement. FCBF in combination with SVM achieved the best results with a classification accuracy of 81.45%, 77.23% and 68.71% in the three scenarios, respectively. Importantly, FCBF determines, based on the feature set, whether a classifier can be learned, and if so, automatically identifies the subset of relevant and non-correlated features. This suggests that FCBF is a powerful method for BCI systems based on MI. Knowledge gained here about procedural combinations has the potential to produce useful BCI tool, that can provide effective motor control for the users.

We present a provably optimal differentially private algorithm for the stochastic multi-arm bandit problem, as opposed to the private analogue of the UCB-algorithm [Mishra and Thakurta, 2015, Tossou andDimitrakakis, 2016] which doesn't meet the recently discovered lower-bound of Ω ( K log(T ) / ) [Shariff and Sheffet, 2018]. Our construction is based on a different algorithm, Successive Elimination [Even-Dar et al., 2002], that repeatedly pulls all remaining arms until an arm is found to be suboptimal and is then eliminated. In order to devise a private analogue of Successive Elimination we visit the problem of private stopping rule, that takes as input a stream of i.i.d samples from an unknown distribution and returns a multiplicative (1 ± α)-approximation of the distribution's mean, and prove the optimality of our private stopping rule. We then present the private Successive Elimination algorithm which meets both the non-private lower bound [Lai and Robbins, 1985] and the above-mentioned private lower bound. We also compare empirically the performance of our algorithm with the private UCB algorithm.

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