Mohamed El Kerdawy
Research Subjects
Mohamed El Kerdawy has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Afnan Hassan
Department of Pharmacology and Toxicology, Faculty of Pharmacy, Modern University for Technology and Information, Cairo 12974, Egypt; Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Cairo 12578, Egypt
1 shared publication - Ashraf Badawi
Center for Learning Technologies, University of Science and Technology, Zewail City, Giza 12578, Egypt
1 shared publication - Doaa Shawky
Engineering Mathematics Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt
1 shared publication - Hatem Fayed
Mathematics Program University of Science and Technology Zewail City of Science and Technology October Gardens 6th of October 12578 Giza Egypt
1 shared publication - Mohamed El Halaby
Cairo University
1 shared publication - Mohamed Maher
Center for Learning Technologies, University of Science and Technology, Zewail City, Giza 12578, Egypt
1 shared publication
Latest Publications
- Open Access
The Automatic Detection of Cognition Using EEG and Facial Expressions
Detecting cognitive profiles is critical to efficient adaptive learning systems that automatically adjust the content delivered depending on the learner’s cognitive states and skills. This study explores electroencephalography (EEG) and facial expressions as physiological monitoring tools to build models that detect two cognitive states, namely, engagement and instantaneous attention, and three cognitive skills, namely, focused attention, planning, and shifting. First, while wearing a 14-channel EEG Headset and being videotaped, data has been collected from 127 subjects taking two scientifically validated cognitive assessments. Second, labeling was performed based on the scores obtained from the used tools. Third, different shallow and deep models were experimented in the two modalities of EEG and facial expressions. Finally, the best performing models for the analyzed states are determined. According to the used performance measure, which is the f-beta score with beta = 2, the best obtained results for engagement, instantaneous attention, and focused attention are EEG-based models with 0.86, 0.82, and 0.63 scores, respectively. As for planning and shifting, the best performing models are facial expressions-based models with 0.78 and 0.81, respectively. The obtained results show that EEG and facial expressions contain important and different cues and features about the analyzed cognitive states, and hence, can be used to automatically and non-intrusively detect them.
Open AccessThe Automatic Detection of Cognition Using EEG and Facial Expressions
Detecting cognitive profiles is critical to efficient adaptive learning systems that automatically adjust the content delivered depending on the learner’s cognitive states and skills. This study explores electroencephalography (EEG) and facial expressions as physiological monitoring tools to build models that detect two cognitive states, namely, engagement and instantaneous attention, and three cognitive skills, namely, focused attention, planning, and shifting. First, while wearing a 14-channel EEG Headset and being videotaped, data has been collected from 127 subjects taking two scientifically validated cognitive assessments. Second, labeling was performed based on the scores obtained from the used tools. Third, different shallow and deep models were experimented in the two modalities of EEG and facial expressions. Finally, the best performing models for the analyzed states are determined. According to the used performance measure, which is the f-beta score with beta = 2, the best obtained results for engagement, instantaneous attention, and focused attention are EEG-based models with 0.86, 0.82, and 0.63 scores, respectively. As for planning and shifting, the best performing models are facial expressions-based models with 0.78 and 0.81, respectively. The obtained results show that EEG and facial expressions contain important and different cues and features about the analyzed cognitive states, and hence, can be used to automatically and non-intrusively detect them.