Neurocognitive Engineering for Systems’ Development - Neurotechnology in National Security and Defense - page 44

insights into nervous system function, with particular attention to individual differ-
ences, so as to design systems that are consistent with “natural” patterns of infor-
mation processing in the human brain. In this light, one could imagine designs that
allow the presentation of information in a manner that limits the neural resources
required for processing and thereby increases the speed of perception and perfor-
mance by accessing, facilitating, and/or augmenting the cognitive style and abilities
of an individual operator. Insights into the neural basis of performance also allow
detection of real-time, moment-to-moment changes in neural activity that can be fed
back into an adaptive system (Thorpe et al. 1996). Such information have been used
to develop proof-of-principle systems that merge EEG classification technologies to
interpret when an operator has seen a significant target within natural environments
(Curran et al. 2009) with automated target recognition systems to improve overall
accuracy and speed of target detection (see Gerson et al. 2006, for an early example).
Current efforts are underway to further improve the measurement and classifica-
tion of perceptual states, signal-to-noise ratio, and detection accuracy of EEG-based
approaches (Lawhern et al. 2012; Marathe et al. 2014). Ultimately, it is envisioned
that this type of technology could be integrated into operationally relevant systems
where target detection is a component of complex operator tasking (see Touryan
et al. 2013, for an early proof-of-concept example).
Of equal importance, insight into the neural basis of performance is leading to an
ability to predict future operator capability. Recently, applications of neural decod-
ing techniques to spatial patterns of activity measured with fMRI (Haynes and Rees
2005; Kamitani and Tong 2005) and high-resolution temporal patterns of neural
activity within EEG (Giesbrecht et al. 2009) have been shown to predict perfor-
mance in a dual-task target detection paradigm. Such results, when taken together
with advanced neurophysiological measurement technologies, suggest the potential
not only to monitor ongoing neurocognitive activity but also to use such measure-
ments to predict possible performance failures, giving systems engineers an oppor-
tunity to design systems that can mitigate the detrimental effect(s) of such errors and
thereby enhance soldier survivability and mission success. Such concepts illustrate
one potential type of application that could emerge from technologies that effectively
integrate operator neural activity in real-time into sociotechnical systems. These
brain-computer interaction technologies are envisioned to push applications beyond
human-computer interfaces and to change the very nature of how people interact
with technology and their environment across a broad range of domains, including
operational performance, education and training, medicine, recreation, and technol-
ogy development (Lance et al. 2012). For example, brain-based technologies will
allow computers, for the first time, to leverage sophisticated analyses of the emo-
tional and cognitive states and processes of the people using them, revolutionizing
the basic interactions people have, not only with the systems they use, but also with
each other (see Lance et al. 2012 for recent discussion).
In summary, rapid advancements in technology coupled with the dynamic, com-
plex nature of the national security environment create novel challenges for the mate-
riel developer. The information-intensive environment and widely accepted approach
of decision superiority and information dominance force the creation of sociotechni-
cal systems that share the cognitive burden between personnel and the systems with