Authors:
Fiza Parveen
and
Arnav Bhavsar
Affiliation:
Indian Institute of Technology Mandi, Mandi, India
Keyword(s):
Mental Workload Classification, XGBoost, Hand Crafted Feature Extraction, Stationary Wavelet Transform, Ensemble Majority Voting.
Abstract:
Mental workload is a crucial aspect of cognitive processing as it reflects how much of our working memory is engaged. Studying n-back tasks of varying complexity, has been a popular way to explore the relationship between mental workload and EEG patterns. However there is still scope of improvement in achieving good performance in such a mapping. In this work, we address the classification of EEG patterns corresponding to different n-back tasks. We use publicly available n-back dataset, comprising 0-back, 2-back, and 3-back tasks to represent low, medium, and high levels of mental workload, respectively. We use wavelet-based signal decomposition technique to compute multi-resolution representation having both time and frequency patterns. This is followed by extracting a variety of hand crafted feature. We train different XGBoost models for two level and three level mental workload classification. Furthermore, we employ ensemble techniques at different levels to better categorize EEG
signals. Our approach also involves finding channels that are most significant for classification of highly complex 2-back and 3-back task EEG data.
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