D. N. Tibarewala

TL;DR: The aim of this study is to analyze the performance of linear discriminant analysis (LDA), quadratic discriminant Analysis (QDA) and K-nearest neighbor (KNN) algorithms in differentiating the raw EEG data obtained, into their associative movement, namely, left-right movement.

TL;DR: A novel approach toward EEG-driven position control of a robot arm is proposed by utilizing motor imagery, P300 and error-related potentials (ErRP) to align the robot arm with desired target position.

TL;DR: A comparative study of different classification methods including linear discriminant analysis (LDA), Quadratic discriminant Analysis (QDA), k-nearest neighbor (KNN) algorithm, linear support vector machine (SVM), radial basis function (RBF) SVM and naive Bayesian classifiers algorithms in differentiating the raw EEG data obtained, into their associative left/right hand movements.

TL;DR: A multi-class discriminating algorithm based on the fusion of interval type-2 fuzzy logic and ANFIS to improve uncertainty handling and the result shows the competitiveness of this algorithm over other standard ones in the domain of non-stationary and uncertain signal data classification.

TL;DR: This article proposes an efficient feature selection technique, realized by means of an evolutionary algorithm, which attempts to overcome some of the shortcomings of several state-of-the-art approaches in this field.