scholarly journals Performance analysis of ensemble methods for multi-class classification of motor imagery EEG signal

2014 ◽  
Author(s):  
Saugat Bhattacharyya ◽  
Amit Konar ◽  
D. N. Tibarewala ◽  
Anwesha Khasnobish ◽  
R. Janarthanan
Keyword(s):  
Performance Analysis ◽  
Motor Imagery ◽  
Ensemble Methods ◽  
Eeg Signal ◽  
Multi Class Classification

Comparison of EEG signal decomposition methods in classification of motor-imagery BCI

2018 ◽  
Vol 77 (16) ◽  
pp. 21305-21327 ◽  
Author(s):  
Eltaf Abdalsalam Mohamed ◽  
Mohd Zuki Yusoff ◽  
Aamir Saeed Malik ◽  
Mohammad Rida Bahloul ◽  
Dalia Mahmoud Adam ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Decomposition Methods ◽  
Signal Decomposition ◽  
Eeg Signal

Energy Entropy Used in Identification Based on Motor Imagery EEG

2011 ◽  
Vol 304 ◽  
pp. 274-278
Author(s):  
Xiao Dan
Keyword(s):  
Motor Imagery ◽  
Eeg Signal ◽  
Analysis Method ◽  
Tongue Movement ◽  
Signal Energy ◽  
Linear Discrimination ◽  
Class Separability ◽  
Energy Entropy ◽  
Eeg Data

Subjects are identified by classifying motor imagery EEG signal. Energy entropy was used to preprocess motor imagery EEG data, and the Fisher class separability criterion was applied to extract features. Finally, classification of of extracted features was performed by a Linear discrimination analysis method. Four types motor imagery EEG of three subjects was classified respectively. The results showed that the average classification accuracy achieved over 85%, and the highest was 88.7% on tongue movement imagery EEG

Sign in / Sign up

Export Citation Format

Share Document