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Adaptive Human-Machine Interfaces and Interactions (HMI²) are closed-loop cyber-physical systems comprising a network of sensors measuring human, environmental and mission parameters, in conjunction with suitable software for adapting the HMI² (command, control and display functions) in response to these real-time measurements. Cognitive HMI² are a particular subclass of these systems, which support dynamic HMI² adaptations based on the user’s cognitive state. These states are estimated in real-time using various neuro-physiological parameters from gaze, cardiorespiratory and brain signals, which are processed by an Adaptive Neuro-Fuzzy Inference System (ANFIS). However, the accuracy and precision of neuro-physiological measurements are affected by a variety of environmental factors and therefore need to be accurately characterised prior to operational use. This paper describes the …