AUTHOR=Chervitha Kunamneni , Sharma Lakhan Dev 
  
TITLE=Lobe-wise cognitive load detection using empirical Fourier decomposition and optimized machine learning
  
JOURNAL=Frontiers in Physiology
  
VOLUME=Volume 16 - 2025
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1700756
  
DOI=10.3389/fphys.2025.1700756
  
ISSN=1664-042X
  
ABSTRACT=IntroductionCognitive load significantly affects neural activity, making its assessment important in neuroscience and human–computer interaction. EEG provides a noninvasive way to monitor brain responses to mental effort. This study explores EEG-based feature extraction and classification methods to accurately assess cognitive load during mental tasks.MethodsEEG signals were recorded from all brain lobes over 4 seconds and decomposed into ten intrinsic mode functions using Empirical Fourier Decomposition (EMFD). Entropy-based features were extracted, and feature reduction was applied. Both lobe-wise and overall classifications were performed using optimized ensemble machine learning (OML) and conventional ML classifiers. The approach was evaluated on the Mental Arithmetic Task (MAT) and Spatial Transcriptomic Multi-View (STEW) datasets.ResultsThe proposed EMFD-based OML framework achieved high accuracy, reaching 97.8% on the MAT dataset and 96.4% on the STEW dataset. Lobe-wise analysis showed strong performance across all brain regions, with the frontal lobe achieving the highest accuracies of 97.8% (MAT) and 96.08% (STEW).DiscussionThe findings demonstrate that EMFD combined with optimized ensemble learning effectively enhances EEG-based cognitive load detection. The consistent performance across datasets confirms the robustness of the method, while lobe-wise analysis highlights the frontal lobe’s key role in cognitive processing. The proposed framework outperforms existing methods and shows strong potential for real-world cognitive monitoring applications.