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In this paper we empirically evaluate feature selection methods for classification of Brain-Computer Interface (BCI) data. We selected five state-of the-art ...
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Using a number of different classifiers, it is observed that feature selection helps with the classification performance, higher classification accuracy is ...
Automated EEG Feature Selection for Brain. Computer ... Most current research projects on the field of Brain Computer Interfaces (BCI) utilize these.
Feature Extraction, Feature Selection and Dimensionality Reduction Techniques for Brain Computer Interfaces By Tian Lan A thesis submitted to
Sep 9, 2024 · This paper proposes a novel, automatic feature selection method for BCI able to leverage both data-dependent and expert knowledge to suppress noisy features.
Exploring feature selection and classification techniques to improve the performance of an electroencephalography-based motor imagery brain–computer interface ...
In this paper we empirically evaluate feature selection methods for classification of Brain-Computer Interface (BCI) data. We selected five state-of the-art ...
Aug 1, 2024 · The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain-computer interfaces (BCIs).
The proposed approach is applicable when the objects to be classified are characterized by a large number of features while a few train samples are available.
Based upon EEG data of an existing BCI system, we present a wrapper method for the automated selection of features. The proposed method combines a genetic ...