{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T11:06:22Z","timestamp":1767870382620,"version":"3.49.0"},"reference-count":68,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Cognitive load significantly affects neural activity, making its assessment important in neuroscience and human\u2013computer 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>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\u2019s key role in cognitive processing. The proposed framework outperforms existing methods and shows strong potential for real-world cognitive monitoring applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphys.2025.1700756","type":"journal-article","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T06:34:00Z","timestamp":1767854040000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Lobe-wise cognitive load detection using empirical Fourier decomposition and optimized machine learning"],"prefix":"10.3389","volume":"16","author":[{"given":"Kunamneni","family":"Chervitha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lakhan Dev","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"114700","DOI":"10.1016\/j.chaos.2024.114700","article-title":"Detection of epileptic seizure using eeg signals analysis based on deep learning techniques","volume":"181","author":"Abdulwahhab","year":"2024","journal-title":"Chaos, Solit. and Fractals"},{"key":"B2","doi-asserted-by":"publisher","first-page":"9072","DOI":"10.1016\/j.eswa.2012.02.040","article-title":"Use of principal component analysis for automatic classification of epileptic eeg activities in wavelet framework","volume":"39","author":"Acharya","year":"2012","journal-title":"Expert Syst. 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