Why the study?
Studies toward multiclass workload identification are scarce, and most existing studies utilize only spectral power features from individual channels while ignoring interchannel features.
Feature fusion and selection significantly improve the accuracy of EEG-based multiclass mental workload identification.
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May aid EEG workload monitoring in clinical settings; extends feature methods but leaves open cardiovascular applications pending validation.
Pei et al. (2020) studied this question.
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