Motor Imagery Driven BCI with Cue-Based Selection of FES Induced Grasps

2013 ◽  
pp. 513-516 ◽  
Author(s):  
Andrej M. Savić ◽  
Nebojša B. Malešević ◽  
Mirjana B. Popović
Keyword(s):  
Motor Imagery ◽  
Selection Of

scholarly journals Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata

2013 ◽  
Vol 52 (2) ◽  
pp. 131-139 ◽  
Author(s):  
Saugat Bhattacharyya ◽  
Abhronil Sengupta ◽  
Tathagatha Chakraborti ◽  
Amit Konar ◽  
D. N. Tibarewala
Keyword(s):  
Feature Selection ◽  
Differential Evolution ◽  
Motor Imagery ◽  
Learning Automata ◽  
Eeg Signals ◽  
Selection Of ◽  
Automatic Feature Selection

scholarly journals Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Yuliang Ma ◽  
Xiaohui Ding ◽  
Qingshan She ◽  
Zhizeng Luo ◽  
Thomas Potter ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Motor Imagery ◽  
Particle Swarm ◽  
Classification Performance ◽  
Support Vector ◽  
Eeg Signals ◽  
Swarm Optimization ◽  
Vector Machines ◽  
Selection Of

Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals.

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