Sharif Digital Repository

Common Spatial Pattern (CSP) is a powerful feature extraction method in brain-computer interface (BCI) systems. However, the CSP method has some deficiencies that limit its beneficiary. First, this method is not useful when data is noisy, and it is necessary to have a large dataset because CSP is inclined to overfit. Second, the CSP method uses just the spatial information of the data, and it cannot incorporate the temporal and spectral information. In this paper, we propose a new CSP-based algorithm which is capable of employing the information in all dimensions of data. Also, by defining the regularization term for each mode of information, we can diminish the noise effects and overfitting... 

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... 

Semi-blind source separation (SBSS) approaches are good alternatives to blind source separation (BSS) approaches in applications in which prior knowledge is available about the sources to be extracted. However, their usage has been limited to two-dimensional data sets so far. Therefore, in case of high-dimensional data sets, approaches such as canonical polyadic decomposition (CPD) have been mostly used as a BSS method. The aim of this work is to address this problem by proposing three novel high-dimensional semi-blind source separation methods in the CPD framework. To this end, our first proposed method termed semi-blind alternative least squares (SBALS) is an extension of alternative least... 

Background and objective: Steady-state visual evoked potential (SSVEP) and rapid serial visual presentation (RSVP) are useful methods in the brain-computer interface (BCI) systems. Hybrid BCI systems that combine these two approaches can enhance the proficiency of the P300 spellers. Methods: In this study, a new hybrid RSVP/SSVEP BCI is proposed to increase the classification accuracy and information transfer rate (ITR) as compared with the other RSVP speller paradigms. In this paradigm, RSVP (eliciting a P300 response) and SSVEP stimulations are presented in such a way that the target group of characters is identified by RSVP stimuli, and the target character is recognized by SSVEP stimuli.... 

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)  

P300 based Speller systems are a typical application of BCIs that can be used as a communication device for users with spinal cord injury. In this paper, three different RSVP paradigms; single, dual and triple RSVP are compared in terms of character detection accuracy and ITR to find the optimal communication method for these users. Three subjects were tested to compare these methods in offline mode. The results of this comparison shows that character detection accuracy for the single, dual and triple RSVP paradigms are 0.78, 0.63 and 0.64, respectively. The mean of ITR for the single, dual and triple RSVP paradigms was also recorded to be 3.6534, 7.72 and 11.5 bit/min, respectively.... 

Learning a foreign language encompasses a process of trial and error that can lead to the progression of the learner's interlanguage, meaning that the learners make use of their language knowledge that receives feedback and brings about progress through diagnosis of the problems and being corrected by the teacher. Therefore, there exists a need to analyze the underlying reasons causing the students to remain taciturn with a low willingness to communicate (WTC) during a language class. There has been much work done investigating the role of the learner's stable personal characteristics (traits) along with their situational changes at a specific time (states) that influence the student's... 

Using 3-D scanned data to analyze and extract pore network plays a vital role in investigation of porous media's characteristics. In this paper, a new simple method is developed to detect pores and throats for analyzing the connectivity and permeability of the network. This automated method utilizes some of the common and well-known image processing functions which are widely accessible by researchers and this has led to an easy algorithm implementation. In this method, after polishing and quality control of images, using city-block distance function and watershed segmentation algorithm, pores and throats are detected and 3-D network is produced. This method can also be applied on 2-D images... 

In this paper, we try to recognize and distinguish different groups of arrhythmia using novel features which have been obtained from the heart rate's phase space in recent years. For this purpose, we used Triangular Phase Space Mapping (TPSM) and Parabolic Phase Space Mapping (PPSM). For recognition, we used three groups of 15 subjects using the Physionet database (Arrhythmia, Congestive Heart Failure (CHF), and Atrial Fibrillation (AF)) with Normal Sinus Rhythm (NSR). The obtained features discriminate arrhythmia from NSR by p