A major field in cognitive neuroscience investigates neuronal correlates of human decision-making processes [1, 2]. Is it
possible to predict a decision before it is actually revealed by the volunteer? In the presented manuscript we use a
standard paradigm from economic behavioral research that proved emotional influences on human decision making: the
Ultimatum Game (UG). In the UG, two players have the opportunity to split a sum of money. One player is deemed the
proposer and the other, the responder. The proposer makes an offer as to how this money should be split between the
two. The second player can either accept or reject this offer. If it is accepted, the money is split as proposed. If rejected,
then neither player receives anything.
In the presented study a real-time fMRI system was used to derive the brain activation of the responder. Using a
Relevance-Vector-Machine classifier it was possible to predict if the responder will accept or reject an offer. The
classification result was presented to the operator 1-2 seconds before the volunteer pressed a button to convey his
decision. The classification accuracy reached about 70% averaged over six subjects.
Functional MR imaging (fMRI) enables to detect different activated brain areas according to the performed
tasks. However, data are usually evaluated after the experiment, which prohibits intra-experiment optimization
or more sophisticated applications such as biofeedback experiments. Using a human-brain-interface (HBI), subjects
are able to communicate with external programs, e.g. to navigate through virtual scenes, or to experience
and modify their own brain activation. These applications require the real-time analysis and classification of
activated brain areas.
Our paper presents first results of different strategies for real-time pattern analysis and classification realized
within a flexible experiment control system that enables the volunteers to move through a 3D virtual scene in
real-time using finger tapping tasks, and alternatively only thought-based tasks.
KEYWORDS: Statistical analysis, Functional magnetic resonance imaging, Magnetic resonance imaging, Scanners, Optical spheres, Brain activation, Data acquisition, Hemodynamics, Visualization, Control systems
The real-time analysis of brain activation using functional MRI data offers a wide range of new experiments such
as investigating self-regulation or learning strategies. However, besides special data acquisition and real-time data
analysing techniques such examination requires dynamic and adaptive stimulus paradigms and self-optimising
MRI-sequences.
This paper presents an approach that enables the unified handling of parameters influencing the different software
systems involved in the acquisition and analysing process. By developing a custom-made Experiment Description
Language (EDL) this concept is used for a fast and flexible software environment which treats aspects like
extraction and analysis of activation as well as the modification of the stimulus presentation. We describe how
extracted real-time activation is subsequently evaluated by comparing activation patterns to previous acquired
templates representing activated regions of interest for different predefined conditions. According to those results
the stimulus presentation is adapted.
The results showed that the developed system in combination with EDL is able to reliably detect and evaluate
activation patterns in real-time. With a processing time for data analysis of about one second the approach is
only limited by the natural time course of the hemodynamic response function of the brain activation.
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