Brain–Computer Interfaces - Index of

68 C. Neuper and G. Pfurtscheller
2.2 How Neurofeedback Works
The effects of neurofeedback techniques can be understood in terms of basic
neurophysiological mechanisms like neuromodulation (e.g. ascending brain stem
modulation of thalamic and limbic systems) and long-term potentiation (LTP) (for
areviewsee[35]). LTP is one way that the brain permanently changes itself to
adapt to new situations. Neurons learn to respond differently to input. For example,
a neuron’s receptor could become more sensitive to signals from another neuron that
provides useful information. LTP is common in the hippocampus and cortex.
Some authors emphasize that neurofeedback augments the brain’s capacity to
regulate itself, and that this self-regulation (rather than any particular state) forms
the basis of its clinical efficacy [16]. This is based on the idea that, during EEG feedback
training, the participant learns to exert neuromodulatory control over the neural
networks mediating attentional processes. Over time, LTP in these networks consolidates
those processes into stable states. This can be compared to learning a motor
task like riding a bicycle or typing. As a person practices the skill, sensory and proprioceptive
(awareness of body position) input initiates feedback regulation of the
motor circuits involved. Over time, the skill becomes more and more automatic.
Hence, a person who learns to move a cursor on a computer screen that displays
his/her EEG band power changes is learning through the same mechanisms as someone
learning to ride a bicycle. In a typical neurofeedback or BCI paradigm, such as
the “basket game” described in more detail below, subjects must direct a falling ball
into the highlighted target (basket) on a computer screen via certain changes of EEG
band power features. Confronted with this task, the participant probably experiences
a period of “trial-and-error” during which various internal processes are “tried” until
the right mental strategies are found to produce the desired movement of the ball. As
the ball moves in the desired direction, the person watches and “feels” him/herself
moving it. Rehearsal of these activities during ongoing training sessions can then
stabilize the respective brain mechanisms and resulting EEG patterns. Over a number
of sessions, the subject probably acquires the skill of controlling the movement
of the ball without being consciously aware of how this is achieved.
3 Training Paradigms for BCI Control
A specific type of mental activity and strategy is necessary to modify brain signals
to use a BCI effectively. Different approaches to training subjects to control particular
EEG signals have been introduced in BCI research. One approach aims to train
users to automatically control EEG components through operant conditioning [36].
Feedback training of slow cortical potentials (SCPs) was used, for instance, to realize
a communication system for completely paralyzed (locked-in) patients [8]. Other
research groups train subjects to control EEG components by the performance of
specific cognitive tasks, such as mental motor imagery (e.g. [37]). A third approach
views BCI research as mainly a problem of machine learning, and emphasizes