Transient Generalization Over Different Presentation Frequencies: Lagged Stimuli and Superposition
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2020-07-24
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en
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Abstract
Attempts to make brain-computer interfaces more generalizable have
seen some success. The generative model of Thielen et al. has been able
to accurately predict responses to unseen stimuli. However, this model
still requires the unseen stimuli to be presented at the same frequency.
This is because the transients are assumed to stay the same in the gen-
erative model, which is a false assumption when the presentation rate
changes. In this thesis, two methods of generalizing transient responses
across di erent presentation rates are investigated. The rst method uses
lagged stimuli: stimuli where the rate is changed by leaving the length of
the stimulus-ON the same, but changing the length of the stimulus-OFF.
The second method uses superposition, i.e. linear addition of responses,
in order to predict transient responses at di erent presentation rates. The
rst method using lagged stimuli showed promise. Classi cation accura-
cies were signi cantly better than chance when predicting responses to
stimuli at a rate that was di erent from the training rate. At the same
time, however, the correlations between the transients of the same event
at di erent rates were not signi cant. The second method using super-
position showed no signi cant results. Some correlations between real
transients and transients that were predicted using superposition were
signi cant, but the accuracies were at chance level.
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Faculteit der Sociale Wetenschappen