Interactive Neuro-Educational Technologies (I-NET): Enhanced training of threat detection for airport luggage screeners

Interactive Neuro-Educational Technologies (I-NET) are designed to increase the pace and efficiency of skill learning by adapting training environments to the skill levels and needs of the individuals. Advanced Brain Monitoring (ABM) explored the feasibility of integrating physiological measures into an interactive adaptive computer-based training system to facilitate mitigations, accelerate skill acquisition and provide quantitative evidence of successful training in tasks relating to airport luggage screening and threat detection. A small pilot study was conducted (N=23) to assess electroencephalographic measures of learning and performance during a threat identification task using X-Ray images designed to be representative of those typically viewed by baggage screeners. Linear regression analysis of trends in EEG Alpha (8–12 Hz) and Theta (3–7 Hz) from stimulus presentation to response for each image revealed effects for Threat Type, Task Order, Stimulus Difficulty and Response Type. Correlation between EEG engagement and workload levels with performance and heart rate and heart rate variability measures in relation to performance were explored. In addition, fixation locked event related potentials (FLERPS) in relation to user responses were investigated by interfacing a commercial eye tracker to the experimental setup.

[1]  G. Pfurtscheller,et al.  Alpha frequency, cognitive load and memory performance , 1993, Brain Topography.

[2]  S. Segalowitz,et al.  P3 topographical change with task familiarization and task complexity. , 2001, Brain research. Cognitive brain research.

[3]  R. Shiffrin,et al.  Controlled and automatic human information processing: I , 1977 .

[4]  John R. Anderson.  Acquisition of cognitive skill. , 1982 .

[5]  Matthias Schlesewsky,et al.  Fractionating language comprehension via frequency characteristics of the human EEG , 2004, Neuroreport.

[6]  David A. Kobus,et al.  Overview of the DARPA Augmented Cognition Technical Integration Experiment , 2004, Int. J. Hum. Comput. Interact..

[7]  F. Boiten,et al.  Event-related desynchronization: the effects of energetic and computational demands. , 1992, Electroencephalography and clinical neurophysiology.

[8]  Roy Stripling,et al.  Automated SAF Adaptation Tool (ASAT) , 2007, HCI.

[9]  John R. Anderson.  Cognitive Psychology and Its Implications , 1980 .

[10]  W. Klimesch,et al.  Theta band power in the human scalp EEG and the encoding of new information , 1996, Neuroreport.

[11]  A. D. Fisk,et al.  Memory as a function of attention, level of processing, and automatization. , 1984, Journal of experimental psychology. Learning, memory, and cognition.

[12]  A. D. Fisk,et al.  Concurrent Automatic and Controlled Visual Search: Can Processing Occur Without Resource Cost? , 1982 .

[13]  David F. Feldon.  The benefits of an efficiency metric in the evaluation of simulationtask performance , 2004 .

[14]  P. Hagoort,et al.  Event-related theta power increases in the human EEG during online sentence processing , 2002, Neuroscience Letters.

[15]  W. Klimesch.  EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[16]  C. Neuper,et al.  EEG alpha band dissociation with increasing task demands. , 2005, Brain research. Cognitive brain research.