Cross-correlation aided support vector machine classifier for classification of EEG signals
Expert Systems with Applications: An International Journal
Multiclass least-squares support vector machines for analog modulation classification
Expert Systems with Applications: An International Journal
Classification of EEG Signals Using Sampling Techniques and Least Square Support Vector Machines
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data
Pattern Recognition Letters
Automated selecting subset of channels based on CSP in motor imagery brain-computer interface system
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Computational Intelligence and Neuroscience - Special issue on processing of brain signals by using hemodynamic and neuroelectromagnetic modalities
Clustering technique-based least square support vector machine for EEG signal classification
Computer Methods and Programs in Biomedicine
Dynamic, location-based channel selection for power consumption reduction in EEG analysis
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Identification of motor imagery tasks through CC-LR algorithm in brain computer interface
International Journal of Bioinformatics Research and Applications
Computer Methods and Programs in Biomedicine
Automatic classification of sleep stages based on the time-frequency image of EEG signals
Computer Methods and Programs in Biomedicine
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Motor imagery (MI) tasks classification provides an important basis for designing brain-computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10-20 system) as a reference channel for the cross-correlation (CC) technique and applies three diverse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested.