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Mrs . Siuly
Sciences
University of Southern Queensland

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  1. Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain computer interface
  2. Comparisons between motor area EEG and all-channels EEG for two algorithms in motor imagery task classification
  3. Identification of motor imagery tasks through CC–LR algorithm in brain computer interface
  4. Analysis and classification of EEG signals
  5. Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface
  6. Developing a logistic regression model with cross-correlation for motor imagery signal recognition
  7. Clustering technique-based least square support vector machine for EEG signal classification
  8. EEG signal classification based on simple random sampling technique with least square support vector machine
  9. Analysis and classification of EEG signals using a hybrid clustering technique
  10. Classification of EEG signals using sampling techniques and least square support vector machines

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Item title
1Classification of EEG signals using sampling techniques and least square support vector machines
2Analysis and classification of EEG signals using a hybrid clustering technique
3Clustering technique-based least square support vector machine for EEG signal classification
4EEG signal classification based on simple random sampling technique with least square support vector machine
5Developing a logistic regression model with cross-correlation for motor imagery signal recognition
6Analysis and classification of EEG signals
7Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface
8Identification of motor imagery tasks through CC–LR algorithm in brain computer interface
9Comparisons between motor area EEG and all-channels EEG for two algorithms in motor imagery task classification
10Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain computer interface