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Open AccessArticle

Characterizing Focused Attention and Working Memory Using EEG

1
Center for Learning Technologies, University of Science and Technology, Zewail City, Giza 12578, Egypt
2
Mathematics Department, Faculty of Science, Cairo University, Giza 12613, Egypt
3
Engineering Mathematics Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 3743; https://doi.org/10.3390/s18113743
Received: 8 October 2018 / Revised: 29 October 2018 / Accepted: 30 October 2018 / Published: 2 November 2018
(This article belongs to the Section Biosensors)
Detecting the cognitive profiles of learners is an important step towards personalized and adaptive learning. Electroencephalograms (EEG) have been used to detect the subject’s emotional and cognitive states. In this paper, an approach for detecting two cognitive skills, focused attention and working memory, using EEG signals is proposed. The proposed approach consists of the following main steps: first, subjects undergo a scientifically-validated cognitive assessment test that stimulates and measures their full cognitive profile while putting on a 14-channel wearable EEG headset. Second, the scores of focused attention and working memory are extracted and encoded for a classification problem. Third, the collected EEG data are analyzed and a total of 280 time- and frequency-domain features are extracted. Fourth, several classifiers were trained to correctly classify and predict three levels (low, average, and high) of the two cognitive skills. The classification accuracies that were obtained on 86 subjects were 84% and 81% for the focused attention and working memory, respectively. In comparison with similar approaches, the obtained results indicate the generalizability and suitability of the proposed approach for the detection of these two skills. Thus, the presented approach can be used as a step towards adaptive learning where real-time adaptation is to be done according to the predicted levels of the measured cognitive skills. View Full-Text
Keywords: cognitive skills measurement; electroencephalography; short-time fourier transform; classification cognitive skills measurement; electroencephalography; short-time fourier transform; classification
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Mohamed, Z.; El Halaby, M.; Said, T.; Shawky, D.; Badawi, A. Characterizing Focused Attention and Working Memory Using EEG. Sensors 2018, 18, 3743.

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