An optimized feature selection and classification method for using electroencephalographic coherence in brain–computer interfaces

2015 ◽  
Vol 18 ◽  
pp. 11-18 ◽  
Author(s):  
Rocio Salazar-Varas ◽  
David Gutiérrez
Keyword(s):  
Feature Selection ◽  
Classification Method ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

scholarly journals New KF-PP-SVM classification method for EEG in brain-computer interfaces

2014 ◽  
Vol 24 (6) ◽  
pp. 3665-3673
Author(s):  
Banghua Yang ◽  
Zhijun Han ◽  
Peng Zan ◽  
Qian Wang
Keyword(s):  
Classification Method ◽  
Brain Computer Interfaces ◽  
Svm Classification ◽  
Computer Interfaces

Feature Selection Combining Filter and Wrapper Methods for Motor-Imagery Based Brain–Computer Interfaces

2021 ◽  
pp. 2150040
Author(s):  
Hao Sun ◽  
Jing Jin ◽  
Ren Xu ◽  
Andrzej Cichocki
Keyword(s):  
Feature Selection ◽  
Motor Imagery ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Feature Selection Method ◽  
Selection Combining ◽  
Filter Method ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Feature Subspace

Motor imagery (MI) based brain–computer interfaces help patients with movement disorders to regain the ability to control external devices. Common spatial pattern (CSP) is a popular algorithm for feature extraction in decoding MI tasks. However, due to noise and nonstationarity in electroencephalography (EEG), it is not optimal to combine the corresponding features obtained from the traditional CSP algorithm. In this paper, we designed a novel CSP feature selection framework that combines the filter method and the wrapper method. We first evaluated the importance of every CSP feature by the infinite latent feature selection method. Meanwhile, we calculated Wasserstein distance between feature distributions of the same feature under different tasks. Then, we redefined the importance of every CSP feature based on two indicators mentioned above, which eliminates half of CSP features to create a new CSP feature subspace according to the new importance indicator. At last, we designed the improved binary gravitational search algorithm (IBGSA) by rebuilding its transfer function and applied IBGSA on the new CSP feature subspace to find the optimal feature set. To validate the proposed method, we conducted experiments on three public BCI datasets and performed a numerical analysis of the proposed algorithm for MI classification. The accuracies were comparable to those reported in related studies and the presented model outperformed other methods in literature on the same underlying data.

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