High performance GA-LDA feature selection model for Brain Computer Interface data

Sahar Alwadei, Mohamed Dahab, Mahmoud Kamel

Abstract


High-Performance Computing (HPC) proved notable performance enhancements in many different applications. The research is considering the Brain-Computer Interface (BCI) data, precisely the P300 based system. BCI is a system that provides a direct communication control channel between the brain and the external world, but its data processing is exceedingly time-consuming. That system consists of many components where Feature Selection is a primary key to its performance. There is a need for search algorithms and classifiers to build a feature selection model using the wrapper approach. Hence, they are the concern of this research where the Genetic Algorithm (GA) implemented as a search algorithm and Linear Discriminant Analysis (LDA) as a classifier. Thus, the model for features selection formed as GA-LDA. The Evolutionary Algorithm (EA) used, GA, estimates an optimal solution and saves an enormous amount of time more than other algorithms such as brute force search. Furthermore, HPC techniques had implemented since the computational power was one of the main obstacles besides the problem's size causing an extensive processing time. This model saves 98.1% of the original time consumed while using common computing facilities. It also maintains an accuracy rate of 72.5% selecting 46.2% of the original features only.

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References


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