The proposed computationally efficient spatio-spectral feature extractor is particularly suitable for applications in which the computational power is limited, such as emerging wearable mobile BCI systems.

No abstract

Space-time coding techniques are widely used in multiple-input multiple-output communication systems to mitigate the effect of multipath fading in wireless channels. An important subset of space-time codes are linear dispersion (LD) codes, which are used for quasi-static Rayleigh flat fading channels when the channel state information (CSI) is only available at the receiver side. In this thesis, we propose a new receiver structure for LD codes. We suggest to use widely-linear minimum-mean-squared-error (WL-MMSE) estimates of the transmitted symbols in lieu of the sufficient statistics for maximum likelihood (ML) detection of these symbols. This structure offers both optimal and suboptimal operation modes. The structures of the proposed receivers in both modes are derived for general LD codes. As special cases, we study two important subsets of LD codes, namely orthogonal and quasi-orthogonal codes, and examine the performance of the proposed receivers for these codes. Hosseinpour, Mahdi Ramezani, and Seyedhossein Seyedmahdi as well as my colleagues:

In a wide range of communication systems, including DS-CDMA and OFDM systems, the signal-of-interest might be corrupted by an improper [1] (also called non circularly symmetric [2]) interfering signal. This paper studies the maximum likelihood (ML) detection of binary signals in the presence of additive improper complex Gaussian noise. Proposing a new measure for noncircularity of complex random variables, we will derive the ML decision rule and its performance based on this measure. It will be shown that the ML detector performs pseudo correlation [1] as well as conventional correlation of the observation to the signals-of-interest. As an alternative solution, we will propose a filter for converting improper signals to proper ones, called circularization filter, and will utilize it together with a conventional matched-filter (MF) to construct an ML detector.

Classification of mental states from electroencephalogram (EEG) signals is used for many applications in areas such as brain-computer interfacing (BCI). When represented in the frequency domain, the multichannel EEG signal can be considered as a two-directional spatio-spectral data of high dimensionality. Extraction of salient features using feature extractors such as the commonly used linear discriminant analysis (LDA) is an essential step for the classification of these signals. However, multichannel EEG is naturally in matrix-variate format, while LDA and other traditional feature extractors are designed for vector-variate input. Consequently, these methods require a prior vectorization of the EEG signals, which ignores the inherent matrix-variate structure in the data and leads to high computational complexity. A matrix-variate formulation of LDA have previously been proposed. However, this heuristic formulation does not provide the Bayes optimality benefits of LDA. The current paper proposes a Bayes optimal matrix-variate formulation of LDA based on a matrix-variate model for the spatio-spectral EEG patterns. The proposed formulation also provides a strategy to select the most significant features among the different rows and columns.