No abstract

No abstract

Classification of electroencephalogram (EEG) data for different motor imagery (MI) tasks is a major concern in the brain-computer interface (BCI) applications. In this paper, an efficient feature extraction scheme is proposed based on the discrete wavelet transform (DWT) of the EEG signal. The EEG data of each channel is windowed into several frames and DWT is performed on each frame of data. Considering only the approximate DWT coefficients, a set of statistical features are extracted, namely wavelet domain energy, entropy, variance, and maximum. In order to reduce the dimension of the proposed feature vector, which is composed of average statistical feature values of all channels, principal component analysis (PCA) is employed. For the purpose of classification, k nearest neighbor (KNN) classifier is employed. Proposed classification scheme not only offers significant reduction in feature dimensionality but also provides satisfactory classification accuracy. For the purpose of performance analysis, publicly available MI dataset IVa of BCI Competition-III is used and a very satisfactory performance is obtained in classifying the MI data in two classes, namely right hand and right foot MI tasks.

<p>The conventional information leakage metrics assume that an adversary has complete knowledge of the distribution of the mechanism used to disclose information correlated with the sensitive attributes of a system. The only uncertainty arises from the specific realizations that are drawn from this distribution. This assumption does not hold in various practical scenarios where an adversary usually lacks complete information about the joint statistics of the private, utility, and the disclosed data. As a result, the typical information leakage metrics fail to measure the leakage appropriately. In this paper, we introduce multiple new versions of the traditional information-theoretic leakage metrics, that aptly represent information leakage for an adversary who lacks complete knowledge of the joint data statistics, and we provide insights into the potential uses of each. We experiment on a real-world dataset to further demonstrate how the introduced leakage metrics compare with the conventional notions of leakage. Finally, we show how privacy-utility optimization problems can be formulated in this context, such that their solutions result in the optimal information disclosure mechanisms, for various applications.  </p>

No abstract