A novel end-to-end deep learning scheme for classifying multi-class motor imagery electroencephalography signals.
Ahmad HassanpourMajid MoradikiaHojjat AdeliSeyed Raouf KhayamiPirooz ShamsinejadbabakiPublished in: Expert Syst. J. Knowl. Eng. (2019)
Keyphrases
- end to end
- multi class
- learning scheme
- motor imagery
- eeg signals
- eeg data
- brain computer interface
- signal processing
- independent component analysis
- learning algorithm
- brain activity
- blind source separation
- pairwise
- multi class classification
- multiple classes
- support vector machine
- healthy subjects
- congestion control
- cost sensitive
- feature selection
- multi class classifier
- binary classification
- base classifiers
- multi class boosting
- binary classifiers
- non stationary
- neural network
- error correcting output codes
- data sets