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Brain-computer interface (BCI) has created a new era in neuroscience. It has improved the life quality of severely disabled patients. It allows them to regain the power of executing will by their cognitive, expressive and affective brain activities. An electroencephalogram (EEG)-based BCI system with wireless manner was developed to extract EEG signals with Emotiv EPOC head set for recognizing the facial actions in this paper. The extracted feature vectors of EEG can be reduced by the Wavelet transform. Then the reduced EEG signals can then be clearly classified into six clusters by means of support vector machine algorithm with Gaussian kernel function. The better correct rates can be obtained by one-order wavelet transform than those got by three-order wavelet transform. In order to get real-time manner to control an electronic system smoothly, the sampling data have to be reduced. If time consumption is considered, we can choice the one-order wavelet transform with 32 samples. The experimental results showed a promising correct rate for the facial-action recognition through the proposed BCI system with real-time manner. [ABSTRACT FROM AUTHOR]

In this study, we proposed a wireless brain-computer interface (BCI) with steady-state visually evoked potentials (SSVEP) to control several devices in a smart living space for paralyzed patients. In this system, we used electroencephalography (EEG) acquisition chip to extract SSVEPs from EEG signals and transform them by using of FFT into frequency domain. Then, these SSVEPs can be converted into commands to control several devices such as lights, television, air-condition, electric bed, wheelchair, and short message services through a Bluetooth on a mobile device for patients. In this system, several flickering patterns with different frequencies were generated. NeuroSky EEG chips were used to capture EEG signals from locations Oz and FP2. The patients gazed these flickering patterns to generate SSVEPs, and then these SSVEPs were extracted from location Oz on their occipital lobe. Additionally, eye-winking signal was captured from location FP2 on forehead to generate an emergency command. Then these signals can be transformed by FFT into frequency domain and then transmitted to the hardware through Bluetooth interface. The advantages of the proposed BCI system are low cost, low power consumption and compact size so that the system can be suitable for the paralytic patients. The experimental results showed that feasible actions can be obtained for the proposed BCI system and control circuit with a practical operating in a smart living space for paralyzed patients. [ABSTRACT FROM AUTHOR]