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

scholarly journals Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets

2020 ◽  
Vol 10 (23) ◽  
pp. 8481
Author(s):  
Cesar Federico Caiafa ◽  
Jordi Solé-Casals ◽  
Pere Marti-Puig ◽  
Sun Zhe ◽  
Toshihisa Tanaka
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Unsupervised Classification ◽  
Decomposition Methods ◽  
Signal Decomposition ◽  
Learning Performance ◽  
Decomposition Approach ◽  
Data Completion ◽  
Machine Learning Applications

In many machine learning applications, measurements are sometimes incomplete or noisy resulting in missing features. In other cases, and for different reasons, the datasets are originally small, and therefore, more data samples are required to derive useful supervised or unsupervised classification methods. Correct handling of incomplete, noisy or small datasets in machine learning is a fundamental and classic challenge. In this article, we provide a unified review of recently proposed methods based on signal decomposition for missing features imputation (data completion), classification of noisy samples and artificial generation of new data samples (data augmentation). We illustrate the application of these signal decomposition methods in diverse selected practical machine learning examples including: brain computer interface, epileptic intracranial electroencephalogram signals classification, face recognition/verification and water networks data analysis. We show that a signal decomposition approach can provide valuable tools to improve machine learning performance with low quality datasets.

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

Classification of Alzheimers’ Dementia by Using Various Signal Decomposition Methods

2021 ◽  
Author(s):  
Ozlem Karabiber Cura ◽  
Gulce Cosku Yilmaz ◽  
Hatice Sabiha Ture ◽  
Aydin Akan
Keyword(s):  
Decomposition Methods ◽  
Signal Decomposition

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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