Recovery of Event Related Potential Signals using Compressive Sensing and Kronecker Technique Braincomputer interfaces (BCIs) are devices that are developed to enable the brain to communicate with a machine directly. These devices usually make use of eventrelated potential (ERP) component of electroencephalography (EEG) signals. BCIs have several applications, but perhaps the most important one is to communicate with the advance neuromuscular patients. P300 Speller is a method that was developed by making use of BCIs and ERPs to make it possible to communicate with a computer through EEG recordings. The sensitive nature of these signals makes it essential to make sure they have a high recovery rate once they have been compressed. Compressive sensing (CS) is a compression method which takes advantage of the potential sparsity of the signals and aims to reconstruct a signal from a smaller number of measurements that is specified by the Nyquist theorem. CS has been studied in various signal processing areas. Because of the low power consumption and the elapsed time for generating CS measurements, CS became as one of the most efficient compression methods. In this work, we study the applicability of CS and its recovery quality for ERP signals. We run the experiments based on random and deterministic sensing matrices and two different sparsifying bases. The simulation results show that the ERP signal is very suitable for CS compression up to 75% compression ratio (CR). For the recovery phase, we investigate the effects of the recently developed preprocessing approach called Kronecker based technique. By using Kroneckerbased technique in recovery, we could recover the original signal with high accuracy up to 30 dB.

Eventrelated potential from EEG for a twostep Identity Authentication System Current problems related to highlevel security access are increasing, leaving organizations and persons unsafe. A recent good candidate to create a robust identity authentication system is based on brain signals recorded with electroencephalograms (EEG). In this paper, EEGbased brain signals of 56 channels, from eventrelated potentials (ERPs), are used for Subject identification. The ERPs are from positive or negative feedbackrelated responses of a P300speller system. The feature extraction part was done with empirical mode decomposition (EMD) extracting 2 intrinsic mode functions (IMFs) per channel, that were selected based on the Minkowski distance. After that, 4 features are computed per IMF; 2 energy features (instantaneous and teager energy) and 2 fractal features (Higuchi and Petrosian fractal dimension). Support vector machine (SVM) was used for the classification stage with an accuracy index computed using 10folds crossvalidation for evaluating the classifierxe2x80x99s performance. Since highdensity EEG information was available, the wellknown backwardelimination and forwardaddition greedy algorithms were used to reduce or increase the number of channels, step by step. Using the proposed method for subject identification from a positive or negative feedbackrelated response and then identify the subject will add a layer to improve the security system. The results obtained show that subject identification is feasible even using a low number of channels: E.g., 0.89 of accuracy using 5 channels with a mixed population and 0.93 with a maleonly population.