Key result
Blood Volume Pulse (BVP) features, such as peak and max features, were significantly distinctive across different cognitive load levels, enabling automatic classification.
Blood Volume Pulse features can be used to index cognitive load levels, enabling automatic classification in intelligent systems.
BVP features may enable wearable cognitive load monitoring; leaves open prospective validation before cardiovascular use.
Physiological responses contain rich affective information even when humans are not expressing any external signs. In this paper, we investigate the use of the Blood Volume Pulse (BVP) signals for indexing cognitive load. An experiment, which introduced cognitive load as a secondary task in a decision making context was conducted in the study. BVP signals were analyzed in order to establish relationships between BVP and cognitive load levels. A set of features (e.g. peak and max features) was found to be significantly distinctive across different cognitive load levels. The identified BVP features can be used to set up machine learning models for the automatic classification of CL levels in intelligent systems.
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Zhou et al. (2017) studied Cognitive load. Blood Volume Pulse (BVP) signals was evaluated on Cognitive load levels. Blood Volume Pulse (BVP) features, such as peak and max features, were significantly distinctive across different cognitive load levels, enabling automatic classification.
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