Can we go faster than world's fastest brain-computer interface: An application of recurrency to EEG2Code Deep Learning
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2021-07-01
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en
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Abstract
In this research the structure of recurrent neural networks was applied to
EEG2Code deep learning. A state of the art high-speed electroencephalogram
(EEG) brain-computer interface (BCI) speller, using a deep convolutional
neural network. The BCI predicts user intention in a noise-tagging
paradigm speller set-up by decoding the EEG data and predicting individual
ashes in the noise code. While EEG2Code prides itself as being the current
world fasts BCI with an information transfer rate (ITR) of 1237 bits/min
with a trial classi cation accuracy of 95:9%. There is still room for improvement
on the level of individual
ash prediction. Therefore I introduce an
extended variant of the EEG2Code deep learning model which makes use
of a recurrent neural network layer. The goal of this layer is to capture the
temporal dependencies that occur in the data when recording user intention
in the hopes of increasing the ability to predict individual
ashes with a
higher degree of certainty. The recurrent neural network used was a simple
recurrent neural network unit. While this addition did not seem to perform
as expected, by increasing the performance of individual
ash prediction
with the initial hyperparameters given, it could serve as a starting point
for future researchers to tweak the hyperparameters in an attempt to truly
capture the temporal dependencies on an individual
ash to
ash basis.
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Faculteit der Sociale Wetenschappen