Brain–Computer Interfaces - Index of

BCIs in the Laboratory and at Home:
The Wadsworth Research Program
Eric W. Sellers, Dennis J. McFarland, Theresa M. Vaughan, and
Jonathan R.Wolpaw
1 Introduction
Many people with severe motor disabilities lack the muscle control that would allow
them to rely on conventional methods of augmentative communication and control.
Numerous studies over the past two decades have indicated that scalp-recorded
electroencephalographic (EEG) activity can be the basis for non-muscular communication
and control systems, commonly called brain–computer interfaces (BCIs)
[55]. EEG-based BCI systems measure specific features of EEG activity and translate
these features into device commands. The most commonly used features are
rhythms produced by the sensorimotor cortex [38, 55, 56, 59], slow cortical potentials
[4, 5, 23], and the P300 event-related potential [12, 17, 46]. Systems based on
sensorimotor rhythms or slow cortical potentials use oscillations or transient signals
that are spontaneous in the sense that they are not dependent on specific sensory
events. Systems based on the P300 response use transient signals in the EEG that
are elicited by specific stimuli.
BCI system operation has been conceptualized in at least three ways. Blankertz
et al. (e.g., [7]) view BCI development mainly as a problem of machine learning (see
also chapter 7 in this book). In this view, it is assumed that the user produces a signal
in a reliable and predictable fashion, and the particular signal is discovered by the
machine learning algorithm. Birbaumer et al. (e.g., [6]) view BCI use as an operant
conditioning task, in which the experimenter guides the user to produce the desired
output by means of reinforcement (see also chapter 9 in this book). We (e.g., [27,
50, 57] see BCI operation as the continuing interaction of two adaptive controllers,
the user and the BCI system, which adapt to each other. These three concepts of
BCI operation are illustrated in Fig. 1. As indicated later in this review, mutual
adaptation is critical to the success of our sensorimotor rhythm (SMR)-based BCI
E.W. Sellers (B)
Department of Psychology, East Tennessee State University, Box 70649, Johnson City, TN 37641,
USA; Laboratory of Neural Injury and Repair, Wadsworth Center, New York State Department of
Health, Albany, NY 12201-0509, USA
e-mail: sellers@etsu.edu
B. Graimann et al. (eds.), Brain–Computer Interfaces, The Frontiers Collection,
DOI 10.1007/978-3-642-02091-9_6, C○ Springer-Verlag Berlin Heidelberg 2010
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