Abstract:
Excessive mental workload effects mental and physical health along with the performance
of individuals. There is a need to monitor the mental workload of operators
performing critical tasks. Electrical signals produced by neural structures in the brain
can be captured through EEG and information about mental state of an operator can
be inferred. The power distribution in various frequency bands of these signals has
been utilized to assess the mental workload. These noisy signals require significant filtering
and preprocessing because of their low signal to noise ratio. A number of factors
such as window size, filter cut-off, etc. influence the accuracy of EEG-based workload
assessment. In this thesis, we analyze the performance of workload assessment pipeline
with respect to these factors on an open-source workload dataset. Moreover, performance
of workload assessment is analyzed using signals acquired from individual lobes
of the brain instead of the entire brain and using different frequency bands instead of
the entire frequency spectrum. Lastly, we also compare the performance of a number
of classifiers for three level workload classification. For the preprocessing stage,
a sliding window of 256 samples with an overlap of a quarter and an Artifact Subspace
Reconstruction (ASR) threshold of 5 provide maximum assessment accuracy of
71.12%. Frontal and occipital lobes of the brain seem to contain the highest workload
related information as they provide an average assessment accuracy of 65.83%. For
frequency bands analysis, our findings validate that and bands are most relevant
for workload assessment as they provide 72.23% assessment accuracy which is highest
among all bands. Finally, Support Vector Machines (SVM) is able to classify mental
workload with an average accuracy of 66.22% which is the highest among the classifiers
compared.
