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.

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.

Temporal-spatial neural activation patterns linked to perceptual encoding of emotional salience.

It is well known that we continuously filter incoming sensory information, selectively allocating attention to what is important while suppressing distracting or irrelevant information. Yet questions remain about spatiotemporal patterns of neural processes underlying attentional biases toward emotionally significant aspects of the world. One index of affectively biased attention is an emotional variant of an attentional blink (AB) paradigm, which reveals enhanced perceptual encoding for emotionally salient over neutral stimuli under conditions of limited executive attention. The present study took advantage of the high spatial and temporal resolution of magnetoencephalography (MEG) to investigate neural activation related to emotional and neutral targets in an AB task. MEG data were collected while participants performed a rapid stimulus visual presentation task in which two target stimuli were embedded in a stream of distractor words. The first target (T1) was a number and the second (T2) either an emotionally salient or neutral word. Behavioural results replicated previous findings of greater accuracy for emotionally salient than neutral T2 words. MEG source analyses showed that activation in orbitofrontal cortex, characterized by greater power in the theta and alpha bands, and dorsolateral prefrontal activation were associated with successful perceptual encoding of emotionally salient relative to neutral words. These effects were observed between 250 and 550 ms, latencies associated with discrimination of perceived from unperceived stimuli. These data suggest that important nodes of both emotional salience and frontoparietal executive systems are associated with the emotional modulation of the attentional blink.

Differential activation of brain regions involved with error-feedback and imitation based motor simulation when observing self and an expert's actions in pilots and non-pilots on a complex glider landing task.

In this fMRI study we investigate neural processes related to the action observation network using a complex perceptual-motor task in pilots and non-pilots. The task involved landing a glider (using aileron, elevator, rudder, and dive brake) as close to a target as possible, passively observing a replay of one's own previous trial, passively observing a replay of an expert's trial, and a baseline do nothing condition. The objective of this study is to investigate two types of motor simulation processes used during observation of action: imitation based motor simulation and error-feedback based motor simulation. It has been proposed that the computational neurocircuitry of the cortex is well suited for unsupervised imitation based learning, whereas, the cerebellum is well suited for error-feedback based learning. Consistent with predictions, pilots (to a greater extent than non-pilots) showed significant differential activity when observing an expert landing the glider in brain regions involved with imitation based motor simulation (including premotor cortex PMC, inferior frontal gyrus IFG, anterior insula, parietal cortex, superior temporal gyrus, and middle temporal MT area) than when observing one's own previous trial which showed significant differential activity in the cerebellum (only for pilots) thought to be concerned with error-feedback based motor simulation. While there was some differential brain activity for pilots in regions involved with both Execution and Observation of the flying task (potential Mirror System sites including IFG, PMC, superior parietal lobule) the majority was adjacent to these areas (Observation Only Sites) (predominantly in PMC, IFG, and inferior parietal loblule). These regions showing greater activity for observation than for action may be involved with processes related to motor-based representational transforms that are not necessary when actually carrying out the task.

Dynamic visuomotor transformation involved with remote flying of a plane utilizes the 'Mirror Neuron' system.

Brain regions involved with processing dynamic visuomotor representational transformation are investigated using fMRI. The perceptual-motor task involved flying (or observing) a plane through a simulated Red Bull Air Race course in first person and third person chase perspective. The third person perspective is akin to remote operation of a vehicle. The ability for humans to remotely operate vehicles likely has its roots in neural processes related to imitation in which visuomotor transformation is necessary to interpret the action goals in an egocentric manner suitable for execution. In this experiment for 3(rd) person perspective the visuomotor transformation is dynamically changing in accordance to the orientation of the plane. It was predicted that 3(rd) person remote flying, over 1(st), would utilize brain regions composing the 'Mirror Neuron' system that is thought to be intimately involved with imitation for both execution and observation tasks. Consistent with this prediction differential brain activity was present for 3(rd) person over 1(st) person perspectives for both execution and observation tasks in left ventral premotor cortex, right dorsal premotor cortex, and inferior parietal lobule bilaterally (Mirror Neuron System) (Behaviorally: 1(st)>3(rd)). These regions additionally showed greater activity for flying (execution) over watching (observation) conditions. Even though visual and motor aspects of the tasks were controlled for, differential activity was also found in brain regions involved with tool use, motion perception, and body perspective including left cerebellum, temporo-occipital regions, lateral occipital cortex, medial temporal region, and extrastriate body area. This experiment successfully demonstrates that a complex perceptual motor real-world task can be utilized to investigate visuomotor processing. This approach (Aviation Cerebral Experimental Sciences ACES) focusing on direct application to lab and field is in contrast to standard methodology in which tasks and conditions are reduced to their simplest forms that are remote from daily life experience.

Automated analysis of sleep-wake state in rats.

A fully automated computer-based sleep scoring system is described and validated for use in rats. The system was designed to emulate visual sleep scoring by using the same basic features of the electroencephalogram (EEG) and electromyogram (EMG), and a similar set of decision-making rules. State indices are calculated for each 5s epoch by combination of amplitudes (microVrms) of 6 filtered EEG frequency bands (EEGlo, d.c.-1.5Hz; delta, 1.5-6Hz; theta, 6-9Hz; alpha, 10.5-15Hz; beta, 22-30Hz; gamma, 35-45Hz; Sigma(EEG)=delta+theta+alpha+beta+gamma) and EMG (10-100Hz) yielding dimensionless ratios: WAKE-index=(EMGxgamma)/theta; NREM-index=(deltaxalpha)/gamma(2); REM-index=theta(3)/(deltaxalphaxEMG); artifact-index=[(2xEEG(lo))+beta]*(gamma/Sigma(EEG)). The index values are re-scaled and normalized, thereby dispensing with the need for animal-specific threshold values. The system was validated by direct comparison with visually scored data in 9 rats. Overall, the computer and visual scores were 96% concordant, which is similar to inter-rater agreement in visual scoring. False-positive error rate was <5%. A re-test protocol in 7 rats confirmed the long-term stability of the system in studies lasting 5 weeks. The system was implemented and further validated in a study of sleep architecture in 7 rats under a 12:12h LD cycle.