EEG-Based Metrics 2018-11-28T15:50:11-08:00
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EEG-Based Metrics

The scientifically validated B-Alert EEG-based Metrics include Cognitive State for engagement and distraction as well as a Workload Metric. Together they provide researchers with objective, consistent, and accurate measures for parameters that are traditionally assessed with subjective scales. When analyzed individually or in relation to each other, the Metrics enable researchers to observe the otherwise unobservable, and incorporate a useful and reliable alternative to analyzing raw EEG data.

Inquire here for a PDF copy of the peer-review publications detailing the development and accuracy of each metric, and/or publications authored by independent researchers worldwide applying the metrics across various categories of research.

The findings of this study and others support the B-Alert EEG-based Metrics as reliable predictors of fatigue-related errors up to ten minutes in advance of any other observable indicator. Similar results were found at a recent UCSD-led study where the B-Alert Metrics were shown to predict driving related accidents, as well as at the University of Arizona where the researchers found the Metrics predicted student errors on standardized math exams.

Dr. Kanav Kahol and researchers at Arizona State University applied the B-Alert X24 to outline the cognitive parameters of expertise and performance across a team of surgical residents. Throughout the study, the surgical resident subjects were evaluated pre- and post-call for cognitive and mental state assessment.

Aviation pilots typically report high immersion training simulators cannot be as effective for learning as live flight training, so the Office of Naval Research asked Systems Technology’s Dr. Dave Klyde to research why. Dr. Klyde’s team acquired EEG data in conjunction with task performance, pilot-vehicle system, and pilot opinion measures to make a comprehensive assessment of how pilots and their physiology change across platforms. The B-Alert EEG-based Metrics for Cognitive States and Cognitive Workload provided real-time insights on each module’s effectiveness. Slides are available here.