Adham Atyabi

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It is a common phenomenon that classification techniques applied to human EEG data are often more successful for some subjects than others. One reason may be that subjects differ in the degree and length of time that they can continue to be engaged with the experimental task at hand. EEG recording can be a time-consuming, tedious and challenging procedure(More)
Multimedia analysis benefits from understanding the emotional content of a scene in a variety of tasks such as video genre classification and content-based image retrieval. Recently, there has been an increasing interest in applying human bio-signals, particularly eye movements, to recognize the emotional gist of a scene such as its valence. In order to(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)
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)
Computer science is entering a new generation. The previous generation was based on abstracting from hardware. The emerging generation comes from abstracting from software and sees all resources as services in a service-oriented architecture (SOA). In a world of services, it is the service that counts for a customer and not the software or hardware(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)
EEG recording involves having subjects sit on a chair for a couple of hours without being allowed to move and being asked to repeatedly perform various mental, computational, motor imaginary or any other tasks for some specific amount of time. This is a time consuming, boring and complicated procedure during which there is no guarantee that the subject will(More)