Abstract
This paper presents a project, which aims to develop a low-cost Brain-Computer Interface (BCI), whose characteristics may allow educational institutions to improve the learning and evaluation methodologies applicable to a specific student. By collecting reliable electroencephalogram (EEG) data, the system will realize a cognitive state monitoring of the learner and will evaluate its brain activity to adapt the content and visualization of the learning material. Two main objectives have been established in order to determine the success of the investigation: Assess the use of contemporaneous low-cost EEG devices and applications as a proper method to obtain reliable results of the students’ cognitive state. Develop signal-processing algorithms that allow identifying the cognitive state of the students as well as their working memory load (WML).
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Arnaldo, R.M. et al. (2018). Computerized Brain Interfaces for Adaptive Learning and Assessment. In: Karwowski, W., Ahram, T. (eds) Intelligent Human Systems Integration. IHSI 2018. Advances in Intelligent Systems and Computing, vol 722. Springer, Cham. https://doi.org/10.1007/978-3-319-73888-8_37
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DOI: https://doi.org/10.1007/978-3-319-73888-8_37
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