Hierarchical MLP for EEG- Based Decoding of Cognitive Load in Multi-Level n-Back Tasks

This paper contributes by performing following steps: (i) extracts a rich multi-domain EEG feature set spanning time, frequency, and time-frequency domains, (ii) applies rigorous non-parametric statistical selection to retain 241 informative channel–feature pairs, (iii) achieves high-accuracy binary and multi-class classification of n-back load levels, and (iv) complements classification with exploratory PLV and Time-Frequency Representation (TFR) analyses that illuminate the underlying neural mechanisms.