Abstract
Cognitive performance dictates how an individual perceives, records, maintains, retrieves, manipulates, uses and expresses information and are provided in any task that the person is involved in, let it be from the simplest to the most complex. Therefore, it is imperative to identify how a person is cognitively engaging specially in tasks such as information acquisition and studying. Given the surge in online education system, this even becomes more important as the visual feedback of student engagement is missing from the loop. To address this issue, the current study proposes a pipeline to detect cognitive performance by analyzing electroencephalogram (EEG) signals using bidirectional multilayer long-short term memory (BML-LSTM). Tested on an EEG brainwave dataset from 10 students while they watched massive open online course video clips, the obtained results using BML-LSTM show an accuracy \({>}95\%\) in detecting cognitive performance which outperforms all previous methods applied on the same dataset.
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Satu, M.S., Rahman, S., Khan, M.I., Abedin, M.Z., Kaiser, M.S., Mahmud, M. (2020). Towards Improved Detection of Cognitive Performance Using Bidirectional Multilayer Long-Short Term Memory Neural Network. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_27
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DOI: https://doi.org/10.1007/978-3-030-59277-6_27
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