Loading...

EEG based Person Identification Using AdaBoost Algorithm

Pakgohar, Amir Pouya | 2018

381 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 51124 (55)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Shamsollahi, Mohammad Bagher
  7. Abstract:
  8. The person identification by Electroencephalographic (EEG) signals has attracted the researchers’ great attention in recent years and lots of investigations have been developed. An identification system seeks to identify a person in a database. The advantage of using EEG signals for person identification is the difficulty in generating artificial signals for imposters. But more works need to be done to use EEG based biometric in real-life and this thesis is one of them. In this project we classify the EEG signals for person identification using AdaBoost algorithm. Adaptive boosting (AdaBoost) is a machine learning technique for pattern classification in which the performance of the weak learners such as k-Nearest Neighbour (k-NN) can be enhanced effectively by using the results of these weak classifiers. We also represent a new way for classification by combining multi-tasks and electrodes as data for each person. So with this new method we have more datasets for everyone. The results of classifications are impressive (100%) with methods for feature selection such as Principal Component Analysis (PCA) and for feature extraction like wavelet transform (WT) and Butterworth filter
  9. Keywords:
  10. Biometrics ; Electroencphalogram ; Adaboost Algorithm ; Identification

 Digital Object List

 Bookmark

No TOC