Using brain waves to asses controllers’ mental workload

Using neurophysiological measures to assess air traffic controllers’ mental state, measuring in real time mental workload, level of training and type of cognitive control

PROJECT: NINA – Neurometric INdicators for ATM

FUNDING SCHEME: SESAR, WPE

DURATION: 2013 – 2015

WEBSITE: http://nina-wpe.eu | http://nina.dblue.it

CONTACTS: simone.pozzi@dblue.it

DESCRIPTION:
The project develops a system that combines a set of neurophysiological parameters (Brain electrical activity – EEG – Hearth activity, and Ocular activity) to understand the mental state of air traffic controllers, detecting problems such as an excessive mental workload or stress, and eventually providing ad-hoc solutions of adaptive automation, such as dynamic filtering, or highlight of high priority events.

OUR ROLE:
Deep Blue is the project coordinator. We contribute with its Human Factors expertise to the development of the system, providing the link between practical everyday ATM operational problems and neurophysiological indicators. We are responsible for the organisation and conduction of the simulations validating the functionality and reliability of the system in a realistic ATM environment (at ENAC research facilities, Toulouse) with professional and student controllers.

 

We are in charge of the design and validation of the adaptive solutions. Deep Blue is responsible for project communication and results dissemination.

THINGS TO KNOW:

  1. The NINA system can be used during controllers’ training, tomonitor the trainee progresses and gained expertise; during the design phase of new procedures or tools, to precisely evaluate their impact on controllers’ workload; on the job, as a monitoring tool able to understand in real time controllers’ mental state and trigger adaptive solutions to support them when needed (e.g. during peak workload situations).
  2. The NINA neurometrics have been developed and specifically adapted to the Air Traffic Control domain needs and type of tasks.
  3. The same neurometrics can be adapted to other domains, such as aviation (aircraft pilots) or healthcare (surgeons).

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