Authors:
Sidratul Moontaha
1
;
Arpita Kappattanavar
1
;
Pascal Hecker
1
;
2
and
Bert Arnrich
1
Affiliations:
1
Digital Health – Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
;
2
audEERING GmbH, Gilching, Germany
Keyword(s):
Wearable EEG, Cognitive Load Classification, Personalized Model, Generalized Model, Brain Asymmetry.
Abstract:
EEG measures have become prominent with the increasing popularity of non-invasive, portable EEG sensors
for neuro-physiological measures to assess cognitive load. In this paper, utilizing a four-channel wearable
EEG device, the brain activity data from eleven participants were recorded while watching a relaxation video
and performing three cognitive load tasks. The data was pre-processed using outlier rejection based on a
movement filter, spectral filtering, common average referencing, and normalization. Four frequency-domain
feature sets were extracted from 30-second windows encompassing the power of δ, θ, α, β and γ frequency
bands, the respective ratios, and the asymmetry features of each band. A personalized and generalized model
was built for the binary classification between the relaxation and cognitive load tasks and self-reported labels.
The asymmetry feature set outperformed the band ratio feature sets with a mean classification accuracy of
81.7% for the personalize
d model and 78% for the generalized model. A similar result for the models from
the self-reported labels necessitates utilizing asymmetry features for cognitive load classification. Extracting
high-level features from asymmetry features in the future may surpass the performance. Moreover, the better
performance of the personalized model leads to future work to update pre-trained generalized models on
personal data.
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