Biocybernetic Adaptation Strategies: Machine awareness of human state for improved operational performance

Human operators interacting with machines or computers continually adapt to the needs of the system ideally resulting in optimal performance. In some cases, however, deteriorated performance is an outcome. Adaptation to the situation is a strength expected of the human operator which is often accomplished by the human through self-regulation of mental state. Adaptation is at the core of the human operator’s activity, and research has demonstrated that the implementation of a feedback loop can enhance this natural skill to improve training and human/machine interaction. Biocybernetic adaptation involves a “loop upon a loop,” which may be visualized as a superimposed loop which senses a physiological signal and influences the operator’s task at some point. Biocybernetic adaptation in, for example, physiologically adaptive automation employs the “steering” sense of “cybernetic,” and serves a transitory adaptive purpose – to better serve the human operator by more fully representing their responses to the system. The adaptation process usually makes use of an assessment of transient cognitive state to steer a functional aspect of a system that is external to the operator’s physiology from which the state assessment is derived. Therefore, the objective of this paper is to detail the structure of biocybernetic systems regarding the level of engagement of interest for adaptive systems, their processing pipeline, and the adaptation strategies employed for training purposes, in an effort to pave the way towards machine awareness of human state for self-regulation and improved operational performance

A Neuroergonomics Approach to Measure Pilot’s Cognitive Incapacitation in the Real World with EEG

Mental overload and mental fatigue are two degraded cognitive states that are known to promote cognitive incapacitation. We adopted a neuroergonomics approach to investigate these states that remain difficult to induce under la-boratory settings thus impeding their measurement. Two experiments were conducted under real flight conditions to respectively measure the electro-physiological correlates of mental fatigue and mental overload with a 32 chan-nel-dry EEG system. Our findings revealed that the occurrence of mental fatigue was related to higher theta and alpha band power. Mental overload was associ-ated with higher beta band power over frontal sites. We performed single trial classification to detect mental fatigue and over-load states. Classification accu-racy reached 76.9% and 89.1%, respectively, in discriminating mental fatigue vs. no fatigue and mental overload vs. low-high load. These preliminary results provide evidence for the feasibility of detecting neural correlates of cognitive fatigue and load during real flight conditions and provide promising perspec-tives on the implementation of neuroadaptive technology especially in the con-text of single pilot-operation