Sharif Digital Repository

About 20 percent of people above 60 years old suffer from tinnitus though no definitive treatment has been found for it. Evaluation of electrical brain activity using Event-Related Potentials (ERPs) is one of the methods to investigate the underlying reasons of tinnitus perception. Previous studies using ERPs suggest that the precognitive memory in tinnitus groups is negatively affected in comparison to the normal hearing groups. In this study, cognitive memory has been assessed using visual and auditory P300 response with oddball paradigm. Fifteen chronic tinnitus subjects and six normal hearing subjects participated in the experiment. T-test with significance level of 0.05 was applied on... 

Event-related potential (ERP) extraction from ongoing electroencephalograph (EEG) and its enhancement is one of the long-established problems in EEG signal processing. Most of the previous studies have focused mainly on the ERP enhancement without considering the multi-dimentional structure of the signal. In order to take advantage of this property, we propose a tensor-based solution with trial-by-trial concatenated ERP data. Then we develop an algorithm based on low-rank Tucker decomposition to detect single trial ERP component with maximized signal to noise ratio (SNR). In other words, by using tensor algebra we consider both self-similarity in intratrials and global correlation in spatial... 

In this paper, a new stimulation protocol is proposed to detect the P300 component. In this protocol visual oddball paradigm is used to evoke Event Related Potentials (ERPs). Two types of stimulation protocol for P300 detection (the proposed and standard protocols) are compared in terms of the R-square coefficient and the amplitude of the P300 component. Statistical analysis (paired t-test) is applied to determine the significant differences between the two protocols. The proposed method can enhance the ability to detect the P300 component in comparison to the common protocol that has been provided so far (standard protocol)  

Common spatial pattern (CSP) analysis and its extensions have been widely used as feature extraction approaches in the brain-computer interfaces (BCIs). However, most of the CSP-based approaches do not use any prior knowledge that might be available about the two conditions (classes) to be classified. Therefore, their applications are limited to datasets that contain enough variance information about the two conditions. For example, in some event-related potential (ERP) detection applications, such as P300 speller, the information is in the time domain but not in the variance of spatial components. To address this problem, first, we present a novel feature extraction method termed extended... 

Conventional polygraphy has several alternatives and one of them is P300-based guilty knowledge test. The purpose of this paper is to apply a new method called empirical mode decomposition (EMD) to extract features from electroencephalogram (EEG) signal. EMD is an appropriate tool to deal with the nonlinear and nonstationary nature of EEG. In the previous studies on the same data set, some morphological, frequency, and wavelet features were extracted only from Pz channel, and used for the detection of guilty and innocent subjects. In this paper, an EMD-based feature extraction was done on EEG recorded signal. Features were extracted from all three recorded channels (Pz, Cz, and Fz) for... 

Artifact removal is an essential part in electroencephalogram (EEG) recording and the raw EEG signals require preprocessing before feature extraction. In this work, we implemented three filtering methods and demonstrated their effects on the performance of different classifiers. Bandpass digital filtering, median filtering and facet method are three preprocessing approaches investigated in this paper. We used data set lib from the BCI competition 2003 for training and testing phase. Our accuracy varied between 80% and 96%. In our work, we demonstrated that the problems of choosing the classifier and preprocessing methods are not independent of each other. Two of our approaches could achieve... 

In this paper, the performance of five classifiers in P300 speller paradigm are compared. Theses classifiers are Linear Support Vector Machine (LSVM), Gaussian Support Vector Machine (RSVM), Neural Network (NN), Fisher Linear Discriminant (FLD), and Kernel Fisher Discriminant (KFD). In classification of P300 waves, there has been a trend to use SVM classifiers. Although they have shown a good performance, in this paper, it is shown that the FLD classifiers outperform the SVM classifiers. FLD classifier uses only ten channels of the recorded electroencephalogram (EEG) signals. This makes them a very good candidate for real-time applications. In addition, FLD approach does not need any... 

Background: A speller system enables disabled people, specifically those with spinal cord injuries, to visually select and spell characters. A problem of primary speller systems is that they are gaze shift dependent. To overcome this problem, a single Rapid Serial Visual Presentation (RSVP) paradigm was initially introduced in which characters are displayed one-by-one at the center of a screen. New method: Two new protocols, Dual and Triple shifted RSVP paradigms, are introduced and compared against the single paradigm. In the Dual and Triple paradigms, two and three characters are displayed at the center of the screen simultaneously, holding the advantage of displaying the target character...