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    Equimatchable regular graphs

    , Article Journal of Graph Theory ; Volume 87, Issue 1 , 2018 , Pages 35-45 ; 03649024 (ISSN) Akbari, S ; Ghodrati, A. H ; Hosseinzadeh, M. A ; Iranmanesh, A ; Sharif University of Technology
    Wiley-Liss Inc  2018
    Abstract
    A graph is called equimatchable if all of its maximal matchings have the same size. Kawarabayashi, Plummer, and Saito showed that the only connected equimatchable 3-regular graphs are K4 and K3, 3. We extend this result by showing that for an odd positive integer r, if G is a connected equimatchable r-regular graph, then G ϵ {Kr+1, Kr,r}. Also it is proved that for an even r, a connected triangle-free equimatchable r-regular graph is isomorphic to one of the graphs C5, C7, and Kr,r. © 2017 Wiley Periodicals, Inc  

    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 Samiee, N ; 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... 

    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) Samiee, N ; 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...