In this paper, we aim to reduce the redundant features of a dataset to improve the accuracy of classification. For this, we have employed Artificial Bee Colony�...
Artificial Bee Colony Based Feature Selection for Motor Imagery ...
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Abstract. Brain-computer Interface (BCI) has widespread use in Neuro- rehabilitation engineering. Electroencephalograph (EEG) based BCI research.
In this paper, we aim to reduce the redundant features of a dataset to improve the accuracy of classification. For this, we have employed Artificial Bee Colony�...
Bibliographic details on Artificial Bee Colony Based Feature Selection for Motor Imagery EEG Data.
Channel selection method based on linear discriminant criteria is used to automatically select the channels with high discriminative powers and the concept�...
Nov 8, 2017 � In this paper, we proposed a novel feature selection method based on the firefly algorithm and learning automata for four-class motor imagery�...
In this paper, we propose a technique for improving the feature extraction and classification stages in EEG-based Brain-Computer Interface (BCI) Systems. The�...
Artificial Bee Colony Algorithm for Single-Trial ... - Sage Journals
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EEG-based motor imagery classification using neuro- fuzzy prediction and wavelet fractal features. J Neurosci Methods. 2010;189:295-302. 12. Pardey J�...
Jul 15, 2022 � The results suggest that the proposed method can be used to extract significant features and improve the classification of motor imagery tasks.
In this article, we propose an alternative approach to define degrees of freedom to improve feature extraction and signal classification for EEG-based�...
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