The goal of this research is to test the potential for neuroadaptive automation to improve response speed to a hazardous event by using a brain-computer interface (BCI) to decode perceptual-motor intention. Seven participants underwent four experimental sessions while measuring brain activity with magnetoencephalograpy. The first three sessions were of a simple constrained task in which the participant was to pull back on the control stick to recover from a perturbation in attitude in one condition and to passively observe the perturbation in the other condition. The fourth session consisted of having to recover from a perturbation in attitude while piloting the plane through the Grand Canyon constantly maneuvering to track over the river below. Independent component analysis was used on the first two sessions to extract artifacts and find an event related component associated with the onset of the perturbation. These two sessions were used to train a decoder to classify trials in which the participant recovered from the perturbation (motor intention) vs. just passively viewing the perturbation. The BCI-decoder was tested on the third session of the same simple task and found to be able to significantly distinguish motor intention trials from passive viewing trials (mean = 69.8%). The same BCI-decoder was then used to test the fourth session on the complex task. The BCI-decoder significantly classified perturbation from no perturbation trials (73.3%) with a significant time savings of 72.3 ms (Original response time of 425.0-352.7 ms for BCI-decoder). The BCI-decoder model of the best subject was shown to generalize for both performance and time savings to the other subjects. The results of our off-line open loop simulation demonstrate that BCI based neuroadaptive automation has the potential to decode motor intention faster than manual control in response to a hazardous perturbation in flight attitude while ignoring ongoing motor and visual induced activity related to piloting the airplane.
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http://dx.doi.org/10.3389/fnhum.2016.00187 | DOI Listing |
Front Neuroergon
April 2022
Neuroadaptive Human-Computer Interaction, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany.
An automated recognition of faces enables machines to visually identify a person and to gain access to non-verbal communication, including mimicry. Different approaches in lab settings or controlled realistic environments provided evidence that automated face detection and recognition can work in principle, although applications in complex real-world scenarios pose a different kind of problem that could not be solved yet. Specifically, in autonomous driving-it would be beneficial if the car could identify non-verbal communication of pedestrians or other drivers, as it is a common way of communication in daily traffic.
View Article and Find Full Text PDFISA Trans
October 2022
The School of Mechatronics and Control Engineering, Hubei Normal University, Huangshi, Hubei, 435002, China. Electronic address:
The adaptive containment control problem with collision avoidance is investigated for unmanned surface vehicles (USVs). At first, the finite time disturbance observer is developed to present the estimation and compensation of external disturbance. Then, incorporating the artificial potential and radial basis function into the new containment strategy, all followers can enter the convex hull spanned by leaders while the collision is avoided.
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September 2022
College of Automation, Qingdao University, Qingdao 266071, PR China.
This paper studies an observer-based neural network position tracking control scheme for induction motors system operating under a field-oriented control scheme with the problem of stochastic disturbance. Firstly, the angular velocity is estimated by the constructed reduced-order observer. Then, the nonlinear functions are approximated by the neural networks and the stochastic Lyapunov functions are chosen to analyze the stability of the system.
View Article and Find Full Text PDFPhys Ther
July 2021
Laboratory of Vestibular NeuroAdaptation, Department of Otolaryngology - Head and Neck Surgery, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
Objective: Deficits in vestibular function increase the risk for falls while turning. However, the clinical assessment of turning in patients with vestibular dysfunction is lacking, and evidence is limited that identifies the effectiveness of vestibular physical therapy in improving turning performance. The purpose of this study was to quantify walking and turning performance during the instrumented Timed "Up & Go" (TUG) test using body-worn inertial measurement units (IMUs).
View Article and Find Full Text PDFFront Neurosci
August 2020
Chair of Cognitive Modelling in Dynamic Systems, Department of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany.
This study presents the integration of a passive brain-computer interface (pBCI) and cognitive modeling as a method to trace pilots' perception and processing of auditory alerts and messages during operations. Missing alerts on the flight deck can result in out-of-the-loop problems that can lead to accidents. By tracing pilots' perception and responses to alerts, cognitive assistance can be provided based on individual needs to ensure they maintain adequate situation awareness.
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