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.

Genetic polymorphisms of ACE I/D, IL-1β G > A and IL-4 VNTR among Egyptian subjects with rheumatoid arthritis.

Background: Various reports examined the contribution of ACE I/D, IL-1β G > A and IL-4 VNTR with the susceptibility to RA but with conflicting findings. The goal of this study is to assess the impact of these three variants with the susceptibility, clinical and biochemical markers in addition to different composite indices of RA.

Subjects And Methods: This case-control survey enclosed 120 RA Egyptian patients who were emulated with 150 healthy controls from the same territory.

Characterizing Focused Attention and Working Memory Using EEG.

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.