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electroencephalographic--eeg
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Diagnosis of early Alzheimer's disease based on EEG source localization and a standardized realistic head model
, Article IEEE Journal of Biomedical and Health Informatics ; Volume 17, Issue 6 , 2013 , Pages 1039-1045 ; 21682194 (ISSN) ; Zahedi, E ; Jalili, M ; Keikhosravi, A ; Vahdat, B. V ; Sharif University of Technology
2013
Abstract
In this paper, distributed electroencephalographic (EEG) sources in the brain have been mapped with the objective of early diagnosis of Alzheimer's disease (AD). To this end, records from a montage of a high-density EEG from 17 early AD patients and 17 matched healthy control subjects were considered. Subjects were in eyes-closed, resting-state condition. Cortical EEG sources were modeled by the standardized low-resolution brain electromagnetic tomography (sLORETA) method. Relative logarithmic power spectral density values were obtained in the four conventional frequency bands (alpha, beta, delta, and theta) and 12 cortical regions. Results show that in the left brain hemisphere, the theta...
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...
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...