Adham Atyabi

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—EEG recording is a time consuming, boring and complicated procedure during which there is no guarantee that the subject will maintain the proper level of concentration on the requested task at all times. This study investigate the impact of various fragments of time on classification performance. The idea is to improve the classification performance by(More)
Subject transfer is a growing area of research in EEG aiming to address the lack of having enough EEG samples required for BCI by using samples originating from individuals or groups of subjects that previously performed similar tasks. This paper investigates the feasibility of two frameworks for enhancing subject transfer through a 90%+ reduction of EEG(More)
This study is focused on improving the classification performance of EEG data through the use of some data restructuring methods. In this study, the impact of having more training instances/samples vs. using shorter window sizes is investigated. The BCI2003 IVa dataset is used to examine the results. The results not surprisingly indicate that, up to a(More)
—EEG signals usually have a high dimensionality which makes it difficult for classifiers to learn the difference of various classes in the underlying pattern in the signal. This paper investigates several evolutionary algorithms used to reduce the dimensionality of the data. The study presents electrode and feature reduction methods based on Genetic(More)
Particle Swarm Optimization (PSO) method is an Evolutionary algorithm, which outperformed other evolutionary algorithms, such as; GA. PSO method is inspired by animal's group work and social behaviors. Particle Swarm Optimization with Area Extension (AEPSO) was introduced to solve the weaknesses of Basic PSO in static, dynamic optimization tasks (i.e. a(More)