Artificial Bee Colony Based Feature Selection for Motor Imagery EEG Data

Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis

“…In 2013, Rakshit et al [168] have used EEG based BCI to decode the various movements related data generated from the motor areas of the brain. One the issues in BCI research is the presence of redundant data in the features of a given data set, they have used an ABC cluster algorithm to reduce the features, and acquired their corresponding values.…”

High performance EEG signal classification using classifiability and the Twin SVM

“…The second optimization problem tries to find a hyperplane that passes through samples of class (−1) and is at a distance of at least one from samples of class (+1). This problem is given by Minimize x i ∈class(−1),i.e.y i =(−1) (12) subject to the constraints w T x i + b + q i ≥ 1, for x i ∈ class(+1), i.e. y i = (+1) (13) While details of the Twin SVM may be found in the original paper [25], one can observe that the relative sizes of the two datasets are immaterial in this formulation.…”

“…During the online phase of operation, the trained classifier maps these intents into actions. A comprehensive review of trends in BCI systems have been presented in [5][6][7][8], whereas processing paradigms can be found in [9][10][11][12].…”

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

“…The tests performed on data of listed companies prove the accuracy of proposed model. R a k s h i t et al [177] proposed ABC based clustering technique in improving the accuracy by reducing the redundancy in feature set. The results prove that ABC helps to enhance the accuracy by reducing the number of redundant features in the data set.…”