Darius Birvinskas

EEG Dataset Reduction and Feature Extraction Using Discrete Cosine Transform

Brain-Computer interface (BCI) systems require intensive signal processing in order to form control signals for electronic devices. The majority of BCI systems work by reading and interpreting cortically evoked electro-potentials across the scalp via an electro-encephalogram (EEG). An important factor affecting the efficiency of BCI is the number of EEG features. To reduce the number of features is an important way to improve the speed. In this paper, we consider application of discrete cosine transform (DCT) on EEG signals. DCT takes correlated input data and concentrates its energy in just first few transform coefficients. This method is used as a feature extraction step and allows data size reduction without losing important information. For classification we are using artificial neural networks with different number of hidden neurons and training functions. We conclude that the method can be successfully used for the feature extraction and dataset reduction.

EEG Dataset Reduction and Classification Using Wave Atom Transform

Brain Computer Interface (BCI) systems perform intensive processing of the electroencephalogram (EEG) data in order to form control signals for external electronic devices or virtual objects. The main task of a BCI system is to correctly detect and classify mental states in the EEG data. The efficiency (accuracy and speed) of a BCI system depends upon the feature dimensionality of the EEG signal and the number of mental states required for control. Feature reduction can help improve system learning speed and, in some cases, classification accuracy. Here we consider Wave Atom Transform (WAT) of the EEG data as a feature reduction method. WAT takes input data and concentrates its energy in a few transform coefficients. WAT is used as a data preprocessing step for feature extraction. We use artificial neural networks (ANNs) for classification and perform research with varying number of neurons in a hidden layer and different network training functions (Levenberg-Marquardt, Conjugate Gradient Backpropagation, Bayesian Regularization). The novelty of the paper is the application of WAT in the EEG data processing. We conclude that the method can be successfully used for feature extraction and dataset feature reduction in the BCI domain.

Methods and Tools of Digital Triage in Forensic Context: Survey and Future Directions

Digital triage is the first investigative step of the forensic examination. The digital triage comes in two forms, live triage and post-mortem triage. The primary goal of the live triage is a rapid extraction of an intelligence from the potential sources. The live triage raises legitimate concerns. The post-mortem triage is conducted in the laboratory and its main goal is ranking of the seized devices for the possible existence of the relevant evidence. The digital triage has the potential to quickly identify items that are likely to contain the evidential data. Therefore, it is a solution to the problem of case backlogs. However, existing methods and tools of the digital triage have limitations, especially, in the forensic context. Nevertheless, we have no better solution for the time being. In this paper, we critically review published research works and the proposed solutions for digital triage. The review is divided into four sections as follows: live triage, post-mortem triage, mobile device triage, and triage tools. We conclude that many challenges are awaiting for the developers in creating methods and tools of digital triage in order to keep pace with the development of new technologies.

Energy Efficient Method for Motor Imagery Data Compression

Electroencephalogram (EEG) is a popular method for measuring the electrical activity of the brain, and diagnose a variety of neurological conditions such as epileptic seizure. Furthermore, most Brain - Computer Interface systems provide modes of communication based on EEG, usually signals are recorded with several electrodes and transmitted through a communication channel for further processing. In order to decrease communication bandwidth and transmission time in portable or low cost devices, data compression is required. In this paper we consider the use of fast Discrete Cosine Transform (DCT) algorithms for lossy EEG data compression. Using this approach, the signal is partitioned into a set of 8 samples and each set is DCT-transformed. The least-significant transform coefficients are removed before transmission and are filled with zeros before an inverse transform. We conclude that this method can be used in low power wireless systems, where low computational complexity and high speed are required.