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Accurate modeling of Electroencephalography (EEG) signals is an important problem in clinical diagnosis of brain diseases. The method using support vectors machine (SVM) based on the structure risk minimization provides us an effective way of learning machine. But solving the quadratic programming problem for training SVM becomes a bottle-neck of using SVM because of the long time of SVM training...
The use of Brain-Computer Interface (BCI) has been increasing exponentially in the recent years due to the use of low-cost commercial Fast Fourier Transform (FFT) based EEG reading devices with non-clinical accuracy for consumer application development. Also, the design and implementation of 3D virtual environments for BCI training purposes has proven to be effective due to the high interaction with...
Eye blinks and lateral eye movements are prominent in EEG signals which are obtained by placing electrodes in the frontal region of the brain. This paper presents a machine learning approach to detect eye movements and blinks from EEG data and map them as intents to control external devices like a computer desktop or a wheel chair.
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