Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique

Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique

<p dir="ltr">The increasing prevalence of non-invasive, portable Electroencephalography (EEG) sensors for neuro-physiological measurements has propelled EEG-based assessments of cognitive load (CL) into the spotlight. In this study, we harnessed the capabilities of a four-channel, we...

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Bibliographic Details
Main Author: Iqbal Hassan (22155274) (author)
Other Authors: Monica Zolezzi (10115698) (author), Hanan Khalil (8344038) (author), Rafif Mahmood Al Saady (17632263) (author), Shona Pedersen (2792278) (author), Muhammad E. H. Chowdhury (14150526) (author)
Published: 2024
Subjects:
Biomedical and clinical sciences
Neurosciences
Engineering
Biomedical engineering
Information and computing sciences
Artificial intelligence
Human-centred computing
Machine learning
Cognitive load
unsupervised machine learning
electroencephalography (EEG)
brain-computer interface (BCI)
Electroencephalography
Real-time systems
Feature extraction
Surveys
Recording
Electrodes
Unsupervised learning
Computer interfaces
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