title: Filter bank common spatial patterns in mental workload estimation.
abstract: 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.
authors.forename: Mahnaz
authors.initials: M
authors.lastname: Arvaneh
authors.forename: Alberto
authors.initials: A
authors.lastname: Umilta
authors.forename: Ian H
authors.initials: IH
authors.lastname: Robertson
mesh_terms: D000328:Adult; D000465:Algorithms; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D008570:Memory, Short-Term / Q000502:physiology*; D033281:Nontherapeutic Human Experimentation; D012815:Signal Processing, Computer-Assisted; D059015:Wireless Technology / Q000295:instrumentation; D016526:Workload; D055815:Young Adult