Brain–Computer Interfaces - Index of

Neurofeedback Training for BCI Control
Christa Neuper and Gert Pfurtscheller
1 Introduction
Brain–computer interface (BCI) systems detect changes in brain signals that reflect
human intention, then translate these signals to control monitors or external devices
(for a comprehensive review, see [1]). BCIs typically measure electrical signals
resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram
(ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and
classification algorithms convert neural activity into the required control signals.
BCI research has focused heavily on developing powerful signal processing and
machine learning techniques to accurately classify neural activity [2–4].
However, even with the best algorithms, successful BCI operation depends significantly
on how well users can voluntarily modulate their neural activity. People
need to produce brain signals that are easy to detect. This may be particularly important
during “real-world” device control, when background mental activity and other
electrical noise sources often fluctuate unpredictably. Learning to operate many
BCI-controlled devices requires repeated practice with feedback and reward. BCI
training hence engages learning mechanisms in the brain.
Therefore, research that explores BCI training is important, and could benefit
from existing research involving neurofeedback and learning. This research should
consider the specific target application. For example, different training protocols and
feedback techniques may be more or less efficient depending on whether the user’s
task is to control a cursor on a computer screen [5–7], select certain characters or
icons for communication [8–11], or control a neuroprosthesis to restore grasping
[12, 13].
Even though feedback is an integral part of any BCI training, only a few
studies have explored how feedback affects the learning process, such as when
people learn to use their brain signals to move a cursor to a target on a computer
C. Neuper (B)
Department of Psychology, University of Graz, Graz, Austria; Institute for Knowledge Discovery,
Graz University of Technology, Graz, Austria
e-mail: christa.neuper@uni-graz.at
B. Graimann et al. (eds.), Brain–Computer Interfaces, The Frontiers Collection,
DOI 10.1007/978-3-642-02091-9_4, C○ Springer-Verlag Berlin Heidelberg 2010
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