Open AccessSession to Session Transfer Learning Method Using Independent Component Analysis with Regularized Common Spatial Patterns for EEG-MI Signals
Author(s) -
Zaineb M. Alhakeem,
Ramzy S. Ali
Publication year - 2019
Publication title -
iraqi journal for electrical and electronic engineering/al-maǧallaẗ al-ʻirāqiyyaẗ al-handasaẗ al-kahrabāʼiyyaẗ wa-al-ilikttrūniyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2078-6069
pISSN - 1814-5892
DOI - 10.37917/ijeee.15.1.2
Subject(s) - session (web analytics) , brain–computer interface , computer science , electroencephalography , independent component analysis , transfer of learning , classifier (uml) , artificial intelligence , pattern recognition (psychology) , speech recognition , feature extraction , motor imagery , interface (matter) , machine learning , psychology , world wide web , bubble , psychiatry , maximum bubble pressure method , parallel computing
Training the user in Brain-Computer Interface (BCI) systems based on brain signals that recorded using Electroencephalography Motor Imagery (EEG-MI) signal is a time-consuming process and causes tiredness to the trained subject, so transfer learning (subject to subject or session to session) is very useful methods of training that will decrease the number of recorded training trials for the target subject. To record the brain signals, channels or electrodes are used. Increasing channels could increase the classification accuracy but this solution costs a lot of money and there are no guarantees of high classification accuracy. This paper introduces a transfer learning method using only two channels and a few training trials for both feature extraction and classifier training. Our results show that the proposed method Independent Component Analysis with Regularized Common Spatial Pattern (ICA-RCSP) will produce about 70% accuracy for the session to session transfer learning using few training trails. When the proposed method used for transfer subject to subject the accuracy was lower than that for session to session but it still better than other methods.