Post-FFT Beamforming in a Pilot-Assisted MIMO-OFDM System

It has been shown that by employing beamforming in the receiver of a Single-Input Multiple-Output-orthogonal frequency division multiplexing (SIMO-OFDM) system its performance has been greatly improved by suppressing co-channel interferences and mitigating fading effects of the channel. To get even greater performance applying a similar beamforming technique to the transmitter side of a Multiple-Input Multiple-Output (MIMO)-OFDM system was further employed. However, while in SIMO-OFDM systems both blind and reference-based methods have been investigated, in MIMO-OFDM systems only algorithms based on blind methods have been proposed so far. In this article we develop a reference-based joint transmit–receive beamforming technique, based on pilot symbols, in a MIMO-OFDM system. Post-FFT scheme is used in both sides as the beamforming method. Appropriate adaptive algorithms are developed to obtain the joint optimal beamforming weights in the receiver by minimizing the error signal between the estimated pilot symbols and their actual values. Receiver weights are used locally whereas transmitter weights are sent to the transmitter through a low-rate feedback channel similar to blind methods. The effect of various factors on the performance of the proposed beamforming structure is considered and it is shown that the joint transmit–receive beamforming is more effective than the sole receive beamforming although the improvement due to transmit beamforming is limited by the total transmit power constraint.     

Epileptic seizure predictability from scalp EEG incorporating constrained blind source separation

Most of the methods for prediction of epilepsy recently reported in the literature are based on the evaluation of chaotic behavior of intracranial electroencephalographic (EEG) recordings. These recordings require intensive surgical operations to implant the electrodes within the brain which are hazardous to the patient. Here, we have developed a novel approach to quantify the dynamical changes of the brain using the scalp EEG. The scalp signals are preprocessed by means of an effective block-based blind source separation (BSS) technique to separate the underlying sources within the brain. The algorithm significantly removes the effect of eye blinking artifacts. An overlap window procedure has been incorporated in order to mitigate the inherent permutation problem of BSS and maintain the continuity of the estimated sources. Chaotic behavior of the underlying sources has then been evaluated by measuring the largest Lyapunov exponent. For our experiments, we provided twenty sets of simultaneous intracranial and scalp EEG recordings from twenty patients. The above recordings have been compared. Similar results were obtained when the intracranial electrodes recorded the electrical activity of the epileptic focus. Our preliminary results show a great improvement when the epileptic focus is not captured by the intracranial electrodes.     

Underdetermined blind source separation of temporomandibular joint sounds

The underdetermined blind source separation problem using a filtering approach is addressed. An extension of the FastICA algorithm is devised which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter achieves source recovery by employing the ll-norm algorithm. Besides, we demonstrate how promising FastICA can be to extract the sources. Furthermore, we illustrate how this scenario is particularly appropriate for the separation of temporomandibular joint (TMJ) sounds.