Abstract
Use of Electroencephalography (EEG) to detect cognitive load is a well-practiced technique. Cognitive load reflects the mental load imparted on a person providing a crucial parameter for applications like personalized education and usability testing. There are several approaches to process the EEG signals and thus choosing an optimal signal processing chain is not a straight forward job. The scenario becomes even more interesting while using commercial low-cost, low resolution EEG devices connected to cloud through Internet of Things (IoT) platform. This paper proposes an optimized signal processing chain offering maximum classification accuracy and minimum computational complexity for measuring the cognitive load using low resolution EEG devices.
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© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Sinharay, A., Chatterjee, D., Pal, A. (2015). Cognitive Load Detection on Commercial EEG Devices: An Optimized Signal Processing Chain. In: Giaffreda, R., et al. Internet of Things. User-Centric IoT. IoT360 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-319-19656-5_14
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DOI: https://doi.org/10.1007/978-3-319-19656-5_14
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