In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

1 This paper describes the OpenViBE software platform which enables to design, test and use Brain-Computer Interfaces. Brain-Computer Interfaces (BCI) are communication systems that enable users to send commands to computers only by means of brain activity. BCI are gaining interest among the Virtual Reality (VR) community since they have appeared as promising interaction devices for Virtual Environments (VE). The key features of the platform are 1) a high modularity, 2) embedded tools for visualization and feedback based on VR and 3D displays, 3) BCI design made available to non-programmers thanks to visual programming and 4) various tools offered to the different types of users. The platform features are illustrated in this paper with two entertaining VR applications based on a BCI. In the first one, users can move a virtual ball by imagining hand movements, while in the second one, they can control a virtual spaceship using real or imagined foot movements. Online experiments with these applications together with the evaluation of the platform computational performances showed its suitability for the design of VR applications controlled with a BCI. OpenViBE is a free software distributed under an open-source license.

8Major challenges must be tackled for brain-computer interfaces to mature into an established communications medium for VR applications, which will range from basic neuroscience studies to developing optimal peripherals and mental gamepads and more efficient brain-signal processing techniques.