title: The Brain Is Faster than the Hand in Split-Second Intentions to Respond to an Impending Hazard: A Simulation of Neuroadaptive Automation to Speed Recovery to Perturbation in Flight Attitude.
abstract: 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.
authors.affiliation: Center for Information and Neural Networks, National Institute of Information and Communications Technology, Osaka UniversityOsaka, Japan; Multisensory Cognition and Computation Laboratory, Universal Communication Research Institute, National Institute of Information and Communications TechnologyKyoto, Japan.
authors.forename: Daniel E
authors.initials: DE
authors.lastname: Callan
authors.affiliation: Multisensory Cognition and Computation Laboratory, Universal Communication Research Institute, National Institute of Information and Communications Technology Kyoto, Japan.
authors.forename: Cengiz
authors.initials: C
authors.lastname: Terzibas
authors.affiliation: Locomobi Inc. Toronto, ON, Canada.
authors.forename: Daniel B
authors.initials: DB
authors.lastname: Cassel
authors.affiliation: Neural Information Analysis Laboratories, Advanced Telecommunications Research Institute Kyoto, Japan.
authors.forename: Masa-Aki
authors.initials: MA
authors.lastname: Sato
authors.affiliation: Center of Excellence in Neuroergonomics, Technology, and Cognition, George Mason University Fairfax, VA, USA.
authors.forename: Raja
authors.initials: R
authors.lastname: Parasuraman
keywords: MEG; aviation; brain computer interface; brain machine interface; decoding; independent component analysis; neuroadaptive automation; neuroergonomics