Brain–Computer Interfaces - Index of

66 C. Neuper and G. Pfurtscheller
screen. BCI operation is a type of neurofeedback application, and understanding
the underlying principles of neurofeedback allows the BCI researcher to adapt the
training process according to operant learning principles. Although empirical data
concerning an “optimal” training setting do not exist, BCI researchers may benefit
from experiences with neurofeedback training. In this chapter, we (i) shortly
describe the underlying process involved in neurofeedback, (ii) review evidence for
feedback effects from studies using neurofeedback and BCI, and (iii) discuss their
implications for the design of BCI training procedures.
2 Principles of Neurofeedback
Almost all definitions of neurofeedback include the learning process and operant/instrumental
conditioning as basic elements of the theoretical model. It is well
established that people can learn to control various parameters of the brain’s electrical
activity through a training process that involves the real-time display of ongoing
changes in the EEG (for a review, see [14, 15]). In such a neurofeedback paradigm,
feedback is usually presented visually, by representing the brain signal on a computer
monitor, or via the auditory or tactile modalities. This raises the question of
how to best represent different brain signals (such as sensorimotor rhythm (SMR),
slow cortical potential (SCP), or other EEG activity). Feedback signals are often
presented in a computerized game-like format [42, 50, 51]. These environments
can help maintain the user’s motivation and attention to the task and guide him/her
to achieve a specified goal (namely, specific changes in EEG activity) by maintaining
a certain “mental state”. Changes in brain activity that reflect successful
neurofeedback training are rewarded or positively reinforced.
Typically, thresholds are set for maximum training effectiveness, and the reward
criteria are based on learning models. This means that the task should be challenging
enough that the user feels motivated and rewarded, and hence does not become
bored or frustrated. Thus, in therapeutic practice, reward thresholds are set so that
the reward is received about 60–70% of the time [16]. Thresholds should be adjusted
as the user’s performance improves.
Neurofeedback methods have been widely used for clinical benefits associated
with the enhancement and/or suppression of particular features of the EEG that
have been shown to correlate with a “normal” state of brain functioning. EEG feedback
has been most intensively studied in epilepsy and attention deficit disorders.
For instance, the control of epileptic seizures through learned enhancement of the
12–15 Hz sensorimotor rhythm (SMR) over the sensorimotor cortex, and through
modulation of slow cortical potentials (SCPs), has been established in a number of
controlled studies [17–19]. Other studies showed that children with attention deficit
hyperactivity disorder (ADHD) improved behavioural and cognitive variables after
frequency (e.g., theta/beta) training or SCP training. In a paradigm often applied in
ADHD, the goal is to decrease activity in the theta band and to increase activity in
the beta band (or to decrease theta/beta ratio) [20, 21].