8,698 research outputs found
Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: why can GMDH do better?
Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring
and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back
-prop and Self Organising Maps) for EEG data classification will be implemented and compared to abductive-based networks, namely GMDH (Group Methods of Data Handling) to show how GMDH can optimally (i.e. noise and accuracy) classify a given set of BCI’s EEG signals. It is shown that GMDH provides such improvements. In this endeavour, EGG classification based on GMDH will be researched for
comprehensible classification without scarifying accuracy.
GMDH is suggested to be used to optimally classify a given
set of BCI’s EEG signals. The other areas related to BCI will
also be addressed yet within the context of this purpose
Distribution of Behaviour into Parallel Communicating Subsystems
The process of decomposing a complex system into simpler subsystems has been
of interest to computer scientists over many decades, for instance, for the
field of distributed computing. In this paper, motivated by the desire to
distribute the process of active automata learning onto multiple subsystems, we
study the equivalence between a system and the total behaviour of its
decomposition which comprises subsystems with communication between them. We
show synchronously- and asynchronously-communicating decompositions that
maintain branching bisimilarity, and we prove that there is no decomposition
operator that maintains divergence-preserving branching bisimilarity over all
LTSs.Comment: In Proceedings EXPRESS/SOS 2019, arXiv:1908.0821
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