Towards Zero Training for Brain-Computer Interfacing Matthias Krauledat*, Michael Tangermann, Benjamin Blankertz, Klaus-Robert Mu¨ ller Machine Learning Laboratory, Berlin Institute of Technology, Berlin, Germany Abstract Electroencephalogram (EEG) signals are highly subject-specific and vary considerably even between recording sessions of the same user within the same experimental paradigm. This challenges a stable operation of Brain-Computer Interface (BCI) systems. The classical approach is to train users by neurofeedback to produce fixed stereotypical patterns of brain activity. In the machine learning approach, a widely adapted method for dealing with those variances is to record a so called calibration measurement on the beginning of each session in order to optimize spatial filters and classifiers specifically for each subject and each day. This adaptation of the system to the individual brain signature of each user relieves from the need of extensive user training. In this paper we suggest a new method that overcomes the requirement of these time- consuming calibration recordings for long-term BCI users. The method takes advantage of knowledge collected in previous sessions: By a novel technique, prototypical spatial filters are determined which have better generalization properties compared to single-session filters. In particular, they can be used in follow-up sessions without the need to recalibrate the system. This way the calibration periods can be dramatically shortened or even completely omitted for these ‘experienced’ BCI users. The feasibility of our novel approach is demonstrated with a series of online BCI experiments. Although performed without any calibration measurement at all, no loss of classification performance was observed. Citation: Krauledat M, Tangermann M, Blankertz B, Mu¨ ller K-R (2008) Towards Zero Training for Brain-Computer Interfacing. PLoS ONE 3(8): e2967. doi:10.1371/ journal.pone.0002967 Editor: Olaf Sporns, Indiana University, United States of America Received April 2, 2008; Accepted July 3, 2008; Published August 13, 2008 Copyright: 2008 Krauledat et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The studies were supported by grants of the Bundesministerium fu¨ r Bildung und Forschung (BMBF), FKZ 01IBE01A and FKZ 16SV2231, the Deutsche Forschungsgemeinschaft (DFG), MU 987/3-1, and by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778. This publication only reflects the authors’ views. Competing Interests: The authors have declared that no competing interests exist. * E-mail: kraulem@cs.tu-berlin.de Introduction A Brain-Computer Interface (BCI) based on electroencephalo- gram (EEG) signals provides a direct communication channel for healthy or disabled users from the brain to a technical device. Through motor imagery or movement intentions brain activity can be voluntarily modulated in a predictable way. A BCI system can detect these alterations in the ongoing EEG and control an application (text-entry system; prosthesis; computer game) accord- ingly. Since no peripheral nerves or muscles need to be involved in this process, BCI technology may be used in assistive technology for paralyzed patients. One classical approach to establish EEG- based control is to set up a system that is controlled by a specific EEG feature which is known to be susceptible to conditioning and to let the subjects learn the voluntary control of that feature in a learning process that can last several weeks. In contrast, in the machine learning approach to BCI [1,2] a statistical analysis of a calibration measurement which is recorded at the beginning of each session is used to adapt the system to the specificities of the user’s current brain signals. This approach allows for an effective performance from the first session on without user training [3,2]. As the signals vary between sessions even for the same user, machine learning based BCI systems rely on the calibration procedure for optimal performance (machine training). To present, the use of machine learning based EEG-BCI systems involves two time-consuming preparational steps at the beginning of every new session. The first one, the montage of an EEG cap, has been largely alleviated by recent hardware advancements (see [4] and the discussion section of this paper). The second step is the recording of calibration data, which we will address with this online study. Especially for patients with impaired concentration ability, this initial calibration reduces the valuable remaining time for controlling a device or computer software in the so called feedback application phase. But even for healthy users, the calibration is an annoying procedure. In an offline study, Krauledat et al. [5] recently proposed a new method for avoiding subject training under conditions that could easily be met in practice. The basic idea of the method is as follows: In the case of long- term BCI users, who repeatedly perform BCI sessions with the same mental tasks, one can exploit data from previous sessions in order to learn most of the calibration parameters. This saves time in the setup of the next session. The present study now extends the offline study in [5] by an online application and evaluation, which will further be called the Zero-Training method. In more detail, we show how to learn good spatial filters and classifiers from data of previous sessions which eliminates the necessity of going through a new phase during each new session (see Figure 1). The method is tested against the standard approach where spatial filters and classifiers are trained anew on the calibration data of a new session. The structure of the paper is the following: In the first subsection of the Methods-section, the common spatial pattern (CSP) method is explained in detail, as CSP is important for our proposed new Zero-Training method. The second subsection shows how so-called prototype patterns can be extracted from previous BCI sessions and how a classifier can be prepared in advance of a PLoS ONE | www.plosone.org 1 August 2008 | Volume 3 | Issue 8 | e2967