Dynamic online target control of SSVEP-based brain–computer interface with multiple commands

Brain–computer interfaces (BCI) present a novel challenge to neuroscientists with their strict requirements for high reliability, real-time analysis and quantitative classification of multiple brain activity patterns. A BCI paradigm which is able to satisfy most of these requirements is the steady-state visual evoked potential (SSVEP) approach in which multiple flickering patterns evoke synchronized steady-state brain activity. In this study, we propose a multi-stage procedure for real-time BCI with an implementation for up to eight commands. Our EEG-based BCI system enables a user to navigate a small car on a screen in real time and to execute additional actions. This approach offers several novel points, such as integrated moving patterns for selective attention and minimal eye movement, as well as an online blind-source separation (BSS) unit for artifact rejection, improved feature selection and a fuzzy classifier. The modular and adaptive structure of the BCI platform allows an extension to an even higher number of commands, as well as to other BCI paradigms. O2P-K1Ø Single-trial based neural prediction of immediate and delayed go/no-go decisions Ryohei P. Hasegawa1,2, Yukako T. Hasegawa1,2, Mark A. Segraves2 1 Neuroscience Research Institute, AIST, Tsukuba, Japan; 2 Department of Neurobiology and Physiology, Northwestern University, USA