# Lobe-wise cognitive load detection using empirical Fourier decomposition and optimized machine learning

## TL;DR

This paper introduces a new method using EEG signals and machine learning to detect cognitive load with high accuracy, focusing on brain lobe activity.

## Contribution

A novel EMFD-based optimized machine learning framework for lobe-wise cognitive load detection with high accuracy.

## Key findings

- The EMFD-based OML framework achieved 97.8% accuracy on the MAT dataset and 96.4% on the STEW dataset.
- The frontal lobe showed the highest cognitive load detection accuracy across both datasets.
- The method outperforms existing approaches and is robust across different datasets.

## Abstract

Cognitive 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.

EEG 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.

The 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).

The 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.