Geometric Deep Learning for the Prediction of Music Liking from Consumer-Grade EEG
Keywords
No Thumbnail Available
Authors
Issue Date
2020-08-01
Language
en
Document type
Journal Title
Journal ISSN
Volume Title
Publisher
Title
ISSN
Volume
Issue
Startpage
Endpage
DOI
Abstract
Abstract
Music evokes meaningful experiences in almost everyone, but musical tastes
are highly dependent on the individual. By making use of a large dataset of EEG
recordings during music listening and associated participant music ratings, the
performance of modern deep neural network models for predicting music liking
from EEG recordings using temporal, spectral and connectivity-based features is
explored. An end-to-end trainable graph neural network based on sinc bandpass
lters and functional connectivity is proposed and successfully tested, using am-
plitude envelope correlations as a di erentiable connectivity measure.
Results show that subtracting the resting state connectivity from the connectiv-
ity during music listening serves as an e ective baselining procedure when training
to predict music ratings across subjects. When using personalised models to pre-
dict music ratings, there is a tendency for di erent models to perform well on the
same subjects, which is not explained by the investigated subject-related features.
A sex classi cation task was used to validate the dataset and model implemen-
tation, which revealed that sex classi cation performance is substantially higher
during music listening than during resting state.
Description
Citation
Faculty
Faculteit der Sociale Wetenschappen