NeuroMonitor ambulatory EEG device: Comparative analysis and its application for cognitive load assessment

We have previously presented a wireless ambulatory EEG device (NeuroMonitor) to non-invasively monitor prefrontal cortex scalp EEG activity in real-life settings. This paper discusses analysis and application of data acquired using this device. We assess the device data against a commercially available, clinical grade Neuroscan SynAmps RT EEG system. For the comparison, temporal statistical measures and Power Spectral Density (PSD) are computed for the simultaneous recordings from both devices from (nearly) identical electrode locations. Although the analog signal processing, sampling, and data recording specifications are slightly different for these devices (e.g., filter specifications, ADC - NeuroMonitor: 16 bit and Neuroscan: 24 bit, electrodes - NeuroMonitor: GS26 Pre-gelled Disposable, Neuroscan: Ag/AgCl reusable EEG disc electrodes), the temporal signals and the PSD of two devices had sufficient correlation. The paper also describes pilot data collection for a test protocol to determine cognitive load using the NeuroMonitor device. For analyzing attention levels for 5 different tasks, EEG rhythms (Alpha, Beta and Theta) are extracted and cognitive load index (CLI) is computed. Results show variations in the PSD of these rhythms with respect to corresponding expected cognitive loads in attention-related and relaxed tasks. This study validates the NeuroMonitor ambulatory EEG device data and shows a use-case for real-life cognitive load studies

ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics

Background: The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics, electronics, and dynamical systems. After the teams shared their progress at the conclusion of an 18-month period, the ONR decided to fund a joint applied project in the Navy that integrated those systems on the subject matter of electronic circuits. The University of Memphis took the lead in integrating these systems in an intelligent tutoring system called ElectronixTutor. This article describes the architecture of ElectronixTutor, the learning resources that feed into it, and the empirical findings that support the effectiveness of its constituent ITS learning resources. Results: A fully integrated ElectronixTutor was developed that included several intelligent learning resources (AutoTutor, Dragoon, LearnForm, ASSISTments, BEETLE-II) as well as texts and videos. The architecture includes a student model that has (a) a common set of knowledge components on electronic circuits to which individual learning resources contribute and (b) a record of student performance on the knowledge components as well as a set of cognitive and non-cognitive attributes. There is a recommender system that uses the student model to guide the student on a small set of sensible next steps in their training. The individual components of ElectronixTutor have shown learning gains in previous decades of research. Conclusions: The ElectronixTutor system successfully combines multiple empirically based components into one system to teach a STEM topic (electronics) to students. A prototype of this intelligent tutoring system has been developed and is currently being tested. ElectronixTutor is unique in its assembling a group of well-tested intelligent tutoring systems into a single integrated learning environment