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    Finite-element analysis of platinum-based cone microelectrodes for implantable neural recording

    , Article 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09, Antalya, 29 April 2009 through 2 May 2009 ; 2009 , Pages 395-398 ; 9781424420735 (ISBN) Zarifi, M. H ; Frounchi, J ; Jahed, N ; Tinati, M. A ; National Institutes of Health, NIH; National Institute of Neurological Disorders and Stroke, NINDS; National Science Foundation, NSF ; Sharif University of Technology
    2009
    Abstract
    There have been significant advances in fabrication of high-density microelectrode arrays using silicon micromachining technology in neural signal recording systems. The interface between microelectrodes and chemical environment brings great interest to researchers working on extracellular stimulation. This interface is quite complex and must be modeled carefully to match experimental results. Computer simulation is a method to increase the knowledge about these arrays and to this end the finite element method provides a strong environment for investigation of relative changes of the electrical field extension surrounding an electrode positioned in chemical environment. In this paper FEM... 

    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...