  EEG-based workload estimation technology provides a real time means of
assessing mental workload. Such technology can effectively enhance the
performance of the human-machine interaction and the learning process. When
designing workload estimation algorithms, a crucial signal processing component
is the feature extraction step. Despite several studies on this field, the
spatial properties of the EEG signals were mostly neglected. Since EEG
inherently has a poor spacial resolution, features extracted individually from
each EEG channel may not be sufficiently efficient. This problem becomes more
pronounced when we use low-cost but convenient EEG sensors with limited
stability which is the case in practical scenarios. To address this issue, in
this paper, we introduce a filter bank common spatial patterns algorithm
combined with a feature selection method to extract spatio-spectral features
discriminating different mental workload levels. To evaluate the proposed
algorithm, we carry out a comparative analysis between two representative types
of working memory tasks using data recorded from an Emotiv EPOC headset which
is a mobile low-cost EEG recording device. The experimental results showed that
the proposed spatial filtering algorithm outperformed the state-of-the
algorithms in terms of the classification accuracy.
