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

Optimizing microsurgical skills with EEG neurofeedback

Background By enabling individuals to self-regulate their brainwave activity in the field of optimal performance in healthy individuals, neurofeedback has been found to improve cognitive and artistic performance. Here we assessed whether two distinct EEG neurofeedback protocols could develop surgical skill, given the important role this skill plays in medicine. Results National Health Service trainee ophthalmic microsurgeons (N = 20) were randomly assigned to either Sensory Motor Rhythm-Theta (SMR) or Alpha-Theta (AT) groups, a randomized subset of which were also part of a wait-list 'no-treatment' control group (N = 8). Neurofeedback groups received eight 30-minute sessions of EEG training. Pre-post assessment included a skills lab surgical procedure with timed measures and expert ratings from video-recordings by consultant surgeons, together with state/trait anxiety self-reports. SMR training demonstrated advantages absent in the control group, with improvements in surgical skill according to 1) the expert ratings: overall technique (d = 0.6, p < 0.03) and suture task (d = 0.9, p < 0.02) (judges' intraclass correlation coefficient = 0.85); and 2) with overall time on task (d = 0.5, p = 0.02), while everyday anxiety (trait) decreased (d = 0.5, p < 0.02). Importantly the decrease in surgical task time was strongly associated with SMR EEG training changes (p < 0.01), especially with continued reduction of theta (4–7 Hz) power. AT training produced marginal improvements in technique and overall performance time, which were accompanied by a standard error indicative of large individual differences. Notwithstanding, successful within session elevation of the theta-alpha ratio correlated positively with improvements in overall technique (r = 0.64, p = 0.047). Conclusion SMR-Theta neurofeedback training provided significant improvement in surgical technique whilst considerably reducing time on task by 26%. There was also evidence that AT training marginally reduced total surgery time, despite suboptimal training efficacies. Overall, the data set provides encouraging evidence of optimised learning of a complex medical specialty via neurofeedback training

Brain-Based Indices for User System Symbiosis

The future generation user system interfaces need to be user-centric which goes beyond user-friendly and includes understanding and anticipating user intentions. We introduce the concept of operator models, their role in implementing user-system symbiosis, and the usefulness of brain-based indices on for instance effort, vigilance, workload and engagement to continuously update the operator model. Currently, the best understood parameters in the operator model are vigilance and workload. An overview of the currently employed brain-based indices showed that indices for the lower workload levels (often based on power in the alpha and theta band of the EEG) are quite reliable, but good indices for the higher workload spectrum are still missing. We argue that this is due to the complex situation when performance stays optimal despite increasing task demands because the operator invests more effort. We introduce a model based on perceptual control theory that provides insight into what happens in this situations and how this affects physiological and brain-based indices.We argue that a symbiotic system only needs to intervene directly in situations of under and overload, but not in a high workload situation. Here, the system must leave the option to adapt on a short notice exclusively to the operator. The system should lower task demands only in the long run to reduce the risk of fatigue or long recovery times. We end by indicating future operator model parameters that can be reflected by brain-based indices