📅 22 January 2026
DOI: 10.26877/asset.v8i1.2833
Deep Learning-Based Classification of Cognitive Workload Using Functional Connectivity Features
Advance Sustainable Science, Engineering and Technology
Universitas Persatuan Guru Republik Indonesia Semarang
📄 Abstract
Cognitive workload plays a vital role in tasks that demand dynamic decision-making, especially under high-risk and time-sensitive conditions. An excessive workload can lead to unexpected and disproportionate risks, whereas insufficient workload may cause disengagement, undermining task performance. This underscores the importance of maintaining an optimal level of mental focus in high-pressure situations to ensure successful task execution. This study leverages deep learning methods alongside functional connectivity measures to classify cognitive workload levels. Using the N-back EEG dataset, functional connectivity metrics such as Phase Locking Value (PLV), Phase Lagging Index (PLI), and Coherency are extracted after data pre-processing. These metrics, characterized as directed or non-directed, enable efficient computational analysis. A convolutional neural network (CNN) classifier is employed to categorize cognitive workload into three levels: low (0-back), medium (2-back), and high (3-back). The CNN-A architecture achieves peak performance with an accuracy of 93.75% using PLV, 87.5% using Coherency, and 68.75% using PLI.
🔖 Keywords
#Cognitive workload; Deep Learning; CNN; EEG; Functional Connectivity; Connectivity Metrics
ℹ️ Informasi Publikasi
Tanggal Publikasi
22 January 2026
Volume / Nomor / Tahun
Volume 8, Nomor 1, Tahun 2026
📝 HOW TO CITE
Vineeta Khemchandani; Alok Singh Chauhan; Shahnaz Fatima; Jalauk Singh Maurya; Abhay Singh Rathaur; Kumar Sharma, Narendra; Daya Shankar Srivastava; Vugar Abdullayev, "Deep Learning-Based Classification of Cognitive Workload Using Functional Connectivity Features," Advance Sustainable Science, Engineering and Technology, vol. 8, no. 1, Jan. 2026.
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