Classification of Motor Imagery and Synchronization of Post-Stroke Patient EEG Signal

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Arifah Ummul Fadiyah ◽  
Esmeralda C. Djamal
Keyword(s):  
Motor Imagery ◽  
Stroke Patient ◽  
Eeg Signal ◽  
Post Stroke

Research on classification of Motor Imagery EEG signal based on CNN architecture

2020 ◽  
Author(s):  
Yingjie Zhang ◽  
Xiaozhong Geng ◽  
Hui Yan
Keyword(s):  
Motor Imagery ◽  
Eeg Signal

Classification of Motor Imagery EEG Signal for Navigation of Brain Controlled Drones

2020 ◽  
pp. 3-12
Author(s):  
Somsukla Maiti ◽  
Anamika ◽  
Atanendu Sekhar Mandal ◽  
Santanu Chaudhury
Keyword(s):  
Motor Imagery ◽  
Eeg Signal

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

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