Brain–Computer Interfaces - Index of

114 B. Blankertz et al.
feedback calibration
supervised
measurement
spontaneous
EEG
offline: calibration (15–35 minutes)
labeled
trials
R R R L L L R L
feature
extraction
feature
extraction
machine
learning
classifier
online: feedback (upto 6 hours)
control
logic
Fig. 1 Overview of a machine-learning-based BCI system. The system runs in two phases. In the
calibration phase, we instruct the participants to perform certain tasks and collect short segments
of labeled EEG (trials). We train the classifier based on these examples. In the feedback phase,
we take sliding windows from a continuous stream of EEG; the classifier outputs a real value that
quantifies the likeliness of class membership; we run a feedback application that takes the output of
the classifier as input. Finally, the user receives the feedback on the screen as, e.g., cursor control
training in the operant conditioning approach [8, 9], the feedback application can
start. Here, the users can actually transfer information through their brain activity
and control applications. In this phase, the system is composed of the classifier that
discriminates between different mental states and the control logic that translates
the classifier output into control signals, e.g., cursor position or selection from an
alphabet.
An overview of the whole process in an ML-based BCI is sketched in Fig. 1.
Note that in alternative applications of BCI technology (see Sections 4.3 and 4.4),
the calibration may need novel nonstandard paradigms, as the sought-after mental
states (like lack of concentration, specific emotions, workload) might be difficult to
induce in a controlled manner.
1.2 Neurophysiological Features
There is a variety of other brain potentials, that are used for brain-computer interfacing,
see Chapter 2 in this book for an overview. Here, we only introduce those
brain potentials, which are important for this review. New approaches of the Berlin
BCI project also exploit the attention-dependent modulation of the P300 component