Machine learning for neurotechnology

Brain Computer Interfacing (BCI) – a modern instantiation of Neurotechnology – aims at making use of brain signals for e.g. the control of objects, spelling, gaming and so on. This talk will first provide a brief overview of Brain Computer Interfaces from a machine learning and signal processing perspective. In particular it shows the wealth, the complexity and the difficulties of the data available, a truely enormous challenge: In real-time a multi-variate very strongly noise contaminated data stream is to be processed and neuroelectric activities are to be accurately decoded in real time. Emphasis is put on a novel computational method for alleviating non-stationarity in data, namely stationary subspace analysis (SSA). Finally, I report in more detail about the Berlin Brain Computer Interface (BBCI) that is based on EEG signals and take the audience all the way from the measured signal, the preprocessing and filtering, the classification to the respective application. BCI as a new channel for man-machine communication is discussed in a clinical setting and for gaming. This is joint work with Benjamin Blankertz, Michael Tangermann, Claudia Sanelli, Carmen Vidaurre, Thorsten Dickhaus, Steven Lemm, Paul von Bünau, Frank Meinecke, Wojciech Wojcikiewicz (TU Berlin), Guido Nolte, Andreas Ziehe, (Fraunhofer FIRST, Berlin) Gabriel Curio, Vadim Nikulin (Charite, Berlin) and further members of the Berlin Brain Computer Interface team, see www.bbci.de