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brain-computer-interface--bci
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Detection of change to SSVEPs using analysis of phase space topological : a novel approach
, Article Neurophysiology ; Volume 51, Issue 3 , 2019 , Pages 180-190 ; 00902977 (ISSN) ; Maghooli, K ; Pisheh, N. F ; Mohammadi, M ; Soroush, P. Z ; Tahvilian, P ; Sharif University of Technology
Springer New York LLC
2019
Abstract
A novel method based on EEG nonlinear analysis and analysis of steady-state visual evoked potentials (SSVEPs) has been processed. The EEG phase space is reconstructed, and some new geometrical features are extracted. Statistical analysis is carried out based on ANOVA, and most significant features are selected and then fed into a multi-class support vector machine (MSVM). Both offline and online phases are considered to fully address SSVEP detection. In the offline mode, the whole design evaluation, feature selection, and classifier training are performed. In the online scenario, the proposed method is evaluated and the detection rate is reported for both phases. Subject-dependent and...
A transfer learning algorithm based on linear regression for between-subject classification of EEG data
, Article 25th International Computer Conference, Computer Society of Iran, CSICC 2020, 1 January 2020 through 2 January 2020 ; 2020 ; Sardouie, S. H ; Foroughmand Aarabi, M. H ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
Classification is the most important part of brain-computer interface (BCI) systems. Because the neural activities of different individuals are not identical, using the ordinary methods of subject-dependent classification, does not lead to high accuracy in betweensubject classification problems. As a result, in this study, we propose a novel method for classification that performs well in between-subject classification. In the proposed method, at first, the subject-dependent classifiers obtained from the train subjects are applied to the test trials to obtain a set of scores and labels for the trials. Using these scores and the real labels of the labeled test trials, linear regression is...
Development of a MATLAB-based toolbox for brain computer interface applications in virtual reality
, Article ICEE 2012 - 20th Iranian Conference on Electrical Engineering, 15 May 2012 through 17 May 2012 ; May , 2012 , Pages 1579-1583 ; 9781467311489 (ISBN) ; Shamsollahi, M. B ; Resalat, S. N ; Sharif University of Technology
2012
Abstract
Brain computer interface (BCI) is a widely used system to assist the disabled and paralyzed people by creating a new communication channel. Among the various methods used in BCI area, motor imagery (MI) is the most popular and the most common one due to its the most natural way of communication for the subject. Some software applications are used to implement BCI systems, and some toolboxes exist for EEG signal processing. In recent years virtual reality (VR) technology has entered into the BCI research area to simulate the real world situations and enhance the subject performance. In this work, a completely MATLAB-based MI-based BCI system is proposed and implemented in order to navigate...
High-speed SSVEP-based BCI: Study of various frequency pairs and inter-sources distances
, Article Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012 ; 2012 , Pages 220-223 ; 9781457721779 (ISBN) ; Saba, V ; Afdideh, F ; Heidarnejad, A ; Sharif University of Technology
IEEE
2012
Abstract
Brain Computer Interface provides a new communication channel for people who have severe brain injuries. Among different types of BCIs, SSVEP-based one has been focused in recent years. In this type of BCI, selection of twinkling frequency of external visual stimulant and the distance between stimulants (in case of more than one stimulant) is so important. In this work, a SSVEP-based BCI with two external stimulants was designed. In order to determine the best twinkling frequency of stimulants and the best distance between them, the classification accuracy for seven different twinkling frequency pairs and five different stimulants distances was calculated. Two methods for feature extraction...
A novel dual and triple shifted RSVP paradigm for P300 speller
, Article Journal of Neuroscience Methods ; Volume 328 , 2019 ; 01650270 (ISSN) ; Shamsollahi, M. B ; Sheikh Hassani, M ; Sharif University of Technology
Elsevier B.V
2019
Abstract
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...
A transfer learning algorithm based on csp regularizations of recorded eeg for between-subject classiftcation
, Article 26th National and 4th International Iranian Conference on Biomedical Engineering, ICBME 2019, 27 November 2019 through 28 November 2019 ; 2019 , Pages 199-203 ; 9781728156637 (ISBN) ; Hajipour Sardouie, S ; Mohammad, H ; Foroughmand Aarabi ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
Feature extraction and classification are the most important parts of BCI systems. The new branch of BCI studies focuses on the design of a classifier that is trained to function properly for each individual. This problem is known as Transfer Learning. In between-subject classification, due to the differences in the neural signals' distribution of different individuals, using the common methods of feature extraction for training the classifier, does not lead to high accuracy for the test subject. As a result, in this study, we present a method for extracting features that perform well in between subjects classifications. The data that we used in this study are EEG signals recorded during...
An efficient hardware implementation for a motor imagery brain computer interface system
, Article Scientia Iranica ; Volume 26, Issue 1 , 2019 , Pages 72-94 ; 10263098 (ISSN) ; Mohammadzade, H ; Chamanzar, A. R ; Shabany, M ; Ghojogh, B ; Sharif University of Technology
Sharif University of Technology
2019
Abstract
Brain Computer Interface (BCI) systems, which are based on motor imagery, enable humans to command artificial peripherals by merely thinking about the task. There is a tremendous interest in implementing BCIs on portable platforms, such as Field Programmable Gate Arrays (FPGAS) due to their low-cost, low-power and portability characteristics. This article presents the design and implementation of a Brain Computer Interface (BCI) system based on motor imagery on a Virtex-6 FPGA. In order to design an accurate algorithm, the proposed method avails statistical learning methods such as Mutual Information (MI), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM). It also uses...
A novel hybrid BCI speller based on RSVP and SSVEP paradigm
, Article Computer Methods and Programs in Biomedicine ; Volume 187 , April , 2020 ; Hajipour Sardouie, S ; Mijani, A ; Sharif University of Technology
Elsevier Ireland Ltd
2020
Abstract
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....
An efficient hardware implementation for a motor imagery brain computer interface system
, Article Scientia Iranica ; Volume 26, Issue 1 , 2019 , Pages 72-94 ; 10263098 (ISSN) ; Mohammadzade, H ; Chamanzar, A ; Shabany, M ; Ghojogh, B ; Sharif University of Technology
Sharif University of Technology
2019
Abstract
Brain Computer Interface (BCI) systems, which are based on motor imagery, enable humans to command artificial peripherals by merely thinking about the task. There is a tremendous interest in implementing BCIs on portable platforms, such as Field Programmable Gate Arrays (FPGAS) due to their low-cost, low-power and portability characteristics. This article presents the design and implementation of a Brain Computer Interface (BCI) system based on motor imagery on a Virtex-6 FPGA. In order to design an accurate algorithm, the proposed method avails statistical learning methods such as Mutual Information (MI), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM). It also uses...
Combination of multiple classifiers with fuzzy integral method for classifying the EEG signals in brain-computer interface
, Article ICBPE 2006 - 2006 International Conference on Biomedical and Pharmaceutical Engineering, Singapore, 11 December 2006 through 14 December 2006 ; 2006 , Pages 157-161 ; 8190426249 (ISBN); 9788190426244 (ISBN) ; Esmaeeli, M ; Shouraki, S. B ; Sharif University of Technology
2006
Abstract
In this paper we study the effectiveness of using multiple classifier combination for EEG signal classification aiming to obtain more accurate results than it possible from each of the constituent classifiers. The developed system employs two linear classifiers (SVM,LDA) fused at the abstract and measurement levels for integrating information to reach a collective decision. For making decision, the majority voting scheme has been used. While at the measurement level, two types of combination methods have been investigated: one used fixed combination rules that don't require prior training and a trainable combination method. For the second type, the fuzzy integral method was used. The...