Routing Mechanisms in Convolutional Neural Networks for EEG Decoding
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2021-06-18
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en
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Abstract
Convolutional neural networks (CNNs) are becoming increasingly
popular tools for decoding electroencephalographic (EEG) data. They
alleviate the need for domain expertise in a typical decoding pipeline
by being able to learn feature representations from raw input. However,
the impact of new deep learning research ideas on EEG decoding
is yet to be fully explored. This bachelor thesis introduces the idea
of routing mechanisms in CNNs for EEG decoding. We present two
mechanisms: routing through optimization and routing through attention.
Each mechanism is implemented within the first layer of a CNN
specifically designed to handle EEG data. Through our experimental
setup, we aim to answer the following question: What is the impact of
each routing mechanism on EEG decoding accuracies? To do so, we
look at our models’ learning process and test for statistically significant
differences in performance between our networks and a baseline implementing
regular convolution. Our experimental results show that both
mechanisms function effectively and our models reach at least 89% decoding
accuracy. However, no statistically significant impact is found
for either mechanism. The work here presented sets a framework that
future research can build upon in order to create an efficient flow of
information through the network.
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Faculteit der Sociale Wetenschappen