0% Complete
صفحه اصلی
/
سی امین کنفرانس بین المللی مهندسی برق
Application of Transfer Learning in Optimized Filter- Bank Regularized CSP to Classification of EEG Signals with Small Dataset
نویسندگان :
M. Moein Esfahani
1
Hossein Sadati
2
1- Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran, Iran
2- Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran, Iran
کلمات کلیدی :
Brain-Computer-Interface،BCI،EEG،Motor Imagery،FBRCSP،Common Spatial Pattern
چکیده :
Application of Brain-Computer Interface (BCI) systems to develop a path between brain and external devices, such as Electroencephalography (EEG) signal acquisition, is extensively under study in regard to brain electrical activities. EEG is an inexpensive brain cognition and imaging method with high temporal resolutions for feature extraction in Motor Imagery tasks. The common spatial pattern (CSP) and its optimized algorithms are effective methods for discriminating and classifying EEG Signals. To classify motor imagery tasks in EEG signals, we need to implement the CSP algorithm to extract features and discriminate spatial patterns based on movement tasks in two-class motor imagery signals. Furthermore, owing to the amount of noise in EEG signals and the limited number of trials per subject, we need to optimize the conventional CSP algorithm by adding a penalty term in the denominator of the CSP cost functions. In this study, due to differences in each subject's neural activities, we employed transfer learning which used the information for other subjects to regulate features of the subject. Additionally, BCI Competition III dataset IVa was analyzed. Furthermore, this study presents the optimized Filter Bank Regularized CSP algorithm with Transfer Learning to perform the classification of the electroencephalography (EEG) motor imagery signals. Moreover, to compare the efficiency of the proposed algorithm, the conventional CSP and the proposed optimized CSP have been weighed, and results for both methods are presented. The results at the end explain that the classification with 10-fold cross-validation in comparison with that of the proposed method achieves approximately 15% and 21% higher accuracy against the R-CSP and conventional CSP, respectively.
لیست مقالات
لیست مقالات بایگانی شده
P300 Evoked Related Potential Detection Based on Integration of Modified HOG and Convolutional Neural Networks
Pedram Havaei - Elham Mahmoudzadeh - Maryam Zekri
Application of Artificial Neural Network on Diagnosing Location and Extent of Disk Space Variations in Transformer Windings Using Frequency Response Analysis
Reza Behkam - Hossein Karami - Mahdi Salay Naderi - Gevork Gharehpetian
Controllable UWB THz Absorber Using a New Single-layer Graphene-based Grating
Mahdieh Bozorgi - Mahmood Rafaei Booket - Mohammad Amin Zolghadr
تخمین غلظت ید و زینان در یک نیروگاه هستهای با استفاده از فیلتر کالمن بیرد تحت شرایط مختلف توان راکتور
حسین زحمتکش - حسین الیاسی
طبقه بندی سکته مغزی در یک سیستم دو بعدی چند فرکانسی با استفاده از امواج مایکروویو و یادگیری عمیق
محسن مهرانیان - محمدسعید ماجدی - امیررضا عطاری
Φ-OTDR Event Classification Using Machine Learning and Optical Signal Processing
Amir Babaoughli - Tohid Alizadeh - Seyed Sadra Kashef
Outage and Sum-Rate Analysis for mCAP-NOMA in Visible Light Communication Under Users' Mobility
Amir Oshtoudan - Seyed Mohammad Sajad Sadough
Bit Error Rate Analysis for a Mixed Underwater OWC-FSO Relaying System in the Presence of Pointing Error
Mahdis Saghaee Jahed - Meysam Ghanbari - Seyed Mohammad Sajad Sadough
Revealing Shadows: Low-Light Image Enhancement Using Self-Calibrated Illumination
Farzaneh Koohestani - Nader Karimi - Shadrokh Samavi
Evaluating the Impact of Operation Scheduling Methods on Microgrid Reliability Using Monte Carlo Simulation
Mahsa Omri - Mohammad Jooshaki - Ali Abbaspour - Mahmud Fotuhi-Firuzabad
بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 42.5.3