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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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Iqbal Hassan (22155274) (author)
مؤلفون آخرون:	Monica Zolezzi (10115698) (author), Hanan Khalil (8344038) (author), Rafif Mahmood Al Saady (17632263) (author), Shona Pedersen (2792278) (author), Muhammad E. H. Chowdhury (14150526) (author)
منشور في:	2024
الموضوعات:	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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Cognitive Load Estimation Using a Hybrid Cluster-Based Unsupervised Machine Learning Technique

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