Why the study?
Current research on quantitative pain measurement using EEG signals had only shown promising results in classifying pain from no-pain states, rather than differentiating specific pain levels.
Does a physiologically-inspired hierarchical classifier using EEG features improve the accuracy of classifying five levels of pain compared to state-of-the-art methods?
Does a physiologically-inspired hierarchical classifier using EEG features improve the accuracy of classifying five levels of pain compared to state-of-the-art methods?
Answers are grounded in this paper and cite their sources.
A novel EEG-based hierarchical classification strategy demonstrates high accuracy in differentiating five distinct levels of pain.
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Afrasiabi et al. (2019) studied Pain (n=44). Physiologically-inspired hierarchical classifier using EEG features vs. State-of-the-art methods was evaluated on Classification accuracy for five classes of pain (p=<0.05).
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