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| 8.
Direct Brain–Computer Communication |
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8.3.
Components of graz BCI |
Two types of experimental sessions are used
in the Graz BCI:
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1)
Training sessions where data are collected to set up a subject-specific
classifier (Picture below, upper panel)
and where no feedback is provided. |
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2)
Test sessions where the classifier is used to classify the
subject’s EEG online while the imagination is requested.
In this session, feedback is given to the subject (Picture
below, lower panel). |
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It should be expected that in BCI experiments, where feedback
is given to the subject, the classification accuracy improves
with increasing number of sessions.
This has, however, not always happened. This may be explained
by the man–machine learning dilemma (MMLD).
MMLD implies that two systems (man and machine) are strongly interdependent
but have to be adapted independently.
The starting point of this adaptation is the training of a "machine"
to recognize certain EEG patterns of a subject. During this phase,
no feedback is given. As soon as feedback is provided, each feedback
results in an adaptation of man to machine: man tries to repeat
success and avoid failure. Wrong feedback can elicit frustration,
a response likely to be associated with a widespread EEG desynchronization,
whereas a correct feedback might lead to a reinforcement of the
specific EEG patterns.
Besides the direct effect of feedback, the visually presented
feedback stimulus itself (bar, cursor, letter, etc.) also can
modify the EEG. Changing distributions of EEG patterns require
adaptation of pattern recognition methods, i.e., adaptation of
machine to man. This, again, influences the system’s performance
responses and, via feedback, influences the behavior of man and
results in further variations of the EEG patterns.
Adaptation of the machine (computer) to the man (brain) is necessary.
For this purpose, the protocol for rapid prototyping was developed.
This means that not only various types of parameter estimation
methods and classification algorithms can be implemented very
fast, but also a classifier can be updated within minutes after
each session with or without feedback.
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Picture:
Schematic display of the two adaptive systems involved
in brain computer interactions. Upper panel: The computer
learnsto recognize different brain states. Lower panel:
Feedback to the subject modifies brain activity. |
Source:
Motor Imagery and Direct Brain–Computer Communication, Gert Pfurtscheller and Christa Neuper |
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