Progress of classification algorithms for motor imagery electroencephalogram signals.
10.7507/1001-5515.202101089
- Author:
Tuo LIU
1
;
Yangyang YE
2
;
Kun WANG
2
;
Lichao XU
2
;
Weibo YI
3
;
Minpeng XU
1
;
Dong MING
1
Author Information
1. School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P.R.China.
2. Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P.R.China.
3. Beijing Machine and Equipment Institute, Beijing 100854, P.R.China.
- Publication Type:Journal Article
- Keywords:
classifiers;
machine learning strategies;
motor imagery electroencephalogram
- MeSH:
Algorithms;
Brain-Computer Interfaces;
Electroencephalography;
Imagery, Psychotherapy;
Imagination;
Machine Learning
- From:
Journal of Biomedical Engineering
2021;38(5):995-1002
- CountryChina
- Language:Chinese
-
Abstract:
Motor imagery (MI), motion intention of the specific body without actual movements, has attracted wide attention in fields as neuroscience. Classification algorithms for motor imagery electroencephalogram (MI-EEG) signals are able to distinguish different MI tasks based on the physiological information contained by the EEG signals, especially the features extracted from them. In recent years, there have been some new advances in classification algorithms for MI-EEG signals in terms of classifiers versus machine learning strategies. In terms of classifiers, traditional machine learning classifiers have been improved by some researchers, deep learning and Riemannian geometry classifiers have been widely applied as well. In terms of machine learning strategies, ensemble learning, adaptive learning, and transfer learning strategies have been utilized to improve classification accuracies or reach other targets. This paper reviewed the progress of classification algorithms for MI-EEG signals, summarized and evaluated the existing classifiers and machine learning strategies, to provide new ideas for developing classification algorithms with higher performance.
