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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Zaineb M. Alhakeem
Iraq University College
Computer Science & IT Electrical & Electronics Engineering

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Fast channel selection method using modified camel travelling behavior algorithm Zaineb M. Alhakeem; Ramzy S. Ali
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 3: June 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i3.24254

Abstract

Brain computer interface (BCI) is a protocol to communicate between the human brain and a device or application using brain signals. These signals translated to useful commands by using features extraction and classification. The most widely used features is the power of alpha and beta rhythms. This type of features gives only 70% of classification accuracy without any extra processing using fixed channels to read the signals. Because the distribution of the power in the brain is not a standard for all people, each one has his own brain power map. A selection algorithm is used to find the best channels that could generate higher power than the fixed ones. Modified camel travelling behavior algorithm is used to select the channels that used to extract the power of alpha and beta bands of motor imagery signals. This algorithm is faster to find the best set of channels, and obtain classification accuracy more than 95% using support vector machine classifier.
Session to session transfer learning using regularized four parameters common spatial pattern method Zaineb M. Alhakeem; Ramzy S. Ali
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.6079

Abstract

Brain computer interface (BCI) has many useful applications to help disabled people that have an active brain with difficulties in movements and speaking. One of these applications is the wheelchair, this device is operated always by just one user no sharing or borrowing the device. One-user applications need features extraction methods with high classification accuracy and small training datasets, the variability of the subjects’ mood during the recorded sessions and the tiredness during the long sessions are serious problems that affect the classification accuracy in these applications. Transfer learning can solve the problem, by recording short and separated sessions for the same subject in different training times or days. The proposed method in this paper uses motor imagery (MI) signals from different recorded sessions by one user to build an acceptable size training dataset. To regularize different recording sessions, four tuning parameters that are independent from each other are generated using a loop, these parameters are used to find the ratios of the covariance matrices. The suggested method gives very good performance using a different number of training samples compared with six different common spatial patterns (CSP) methods using only two channels.
Co-Authors Ramzy S. Ali Ramzy S. Ali