Titlebook: Brain-Computer Interfaces; Current Trends and A Aboul Ella Hassanien,Ahmad Taher Azar Book 2015 Springer International Publishing Switzerla

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Digit Recognition System Using EEG Signalrovide a secure communication interface. EEG recordings of six male right-handed subjects in the age group of (20–25) were taken. The subjects were normal without any mental disorder. They did not have any problem in communicating and had normal vision. All subjects have good knowledge of digits. A

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Entgrenzungen und Globalisierungue advantages. This chapter will present the review of the studies on the noninvasive electromagnetic methods for brain monitoring, diagnosis and treatment. The chapter will try to present the detail technical aspects of different electromagnetic brain monitoring modalities (EBMM), their application

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Rationalisierung und (Selbst-)Qualifizierunget decomposition of EEG signals. These features have been used as an input to multiclass least squares support vector machine (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for classification of human emotions from EEG signals. The

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Entgrenzungen und Globalisierungpendent high output channels with customisable output voltages. The flicker frequencies can be easily customised within the frequency range of 5–50 Hz, using a look-up table. The LED flickers are generated with single RGB LEDs which generate the required colour or frequency combinations for combined

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