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Common Spatial Pattern (CSP) is one of the popular and effective methods for discriminating two class electroencephalogram (EEG) measurements. Its probabilistic counterpart by resolving the problem of overfitting as the main limitation of CSP attracted much attention, especially in the motor imaginary based brain computer interface (BCI) applications. Since the computational efficiency is a paramount...
Recently, Motor Imagery (MI) based on Brain Computer Interface (BCI) systems have been noticed for neuro-rehabilitation methods. The challenging key is to correct detection of MI tasks. Probabilistic Common Spatial Pattern (P-CSP) is the most recent and efficient method for discriminating two classes of electroencephalogram (EEG) from Motor Imagery (MI) Task. P-CSP resolves the overfitting which is...
The correct recognition of Motor Imagery task in Brain-Computer Interface (BCI) systems has been an important issue in recent studies. In this study, we propose a classification framework based on ensemble methods to handle spectral and spatial EEG signal characteristics. A mixture of two ensemble classifiers has been used for combining multiple information sources. The performance of the proposed...
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