Brain-computer Interface Framework Research Articles

An application of wireless brain–computer interface for drowsiness detection

Wirelessly networked systems of sensors could enable revolutionary applications at the intersection of biomedical science, networking and control systems. It has a strong potential to take ahead the applications of wireless sensor networks. In this paper, a wireless brain computer interface (BCI) framework for drowsiness detection is proposed, which uses electroencephalogram (EEG) signals produced from the brain wave sensors. The proposed BCI framework comprises of a braincap containing EEG sensors, wireless signal acquisition unit and a signal processing unit. The signal processing unit continuously monitor the preprocessed EEG signals and to trigger a warning tone if a drowsy state happens. This experimental setup provides longer time EEG monitoring and drowsiness detection by incorporating the clustering mechanism into the wireless networks.

A NOVEL APPROACH FOR CONFIGURING THE STIMULATOR OF A BCI FRAMEWORK USING XML

In a working BCI framework, all aspects must be considered as an integral part that contributes to the successful operation of a BCI system. This also includes the development of robust but flexible stimulator, especially the one that closely related to the feedback of a BCI system. This paper describes a novel approach in providing flexible visual stimulator using XML which has been applied for a BCI (brain-computer interface) framework. Using XML file format for configuring the visual stimulator of a BCI system, we can develop BCI applications which can accommodate many experiment strategies in BCI research. The BCI framework and its configuration platform is developed using C++ programming language which incorporate Qt’s most powerful XML parser named QXmlStream. The implementation and experiment shows that the XML configuration file can be well executed within the proposed BCI framework. Beside its capability in presenting flexible flickering frequencies and text formatting for SSVEP-based BCI, the configuration platform also provides 3 shapes, 16 colors, and 5 distinct feedback bars. It is not necessary to increase the number of shapes nor colors since those parameters are less important for the BCI stimulator. The proposed method can then be extended to enhance the usability of currently existed BCI framework such as BF++ Toys and BCI 2000.