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Objective.Sensorimotor decisions require the brain to process external information and combine it with relevant knowledge prior to actions. In this study, we explore the neural predictors of motor actions in a novel, realistic driving task designed to study decisions while driving.Approach.Through a spatiospectral assessment of functional connectivity during the premotor period, we identified the organization of visual cortex regions of interest into a distinct scene processing network. Additionally, we identified a motor action selection network characterized by coherence between the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC).Main results.We show that steering behavior can be predicted from oscillatory power in the visual cortex, DLPFC, and ACC. Power during the premotor periods (specific to the theta and beta bands) correlates with pupil-linked arousal and saccade duration.Significance.We interpret our findings in the context of network-level correlations with saccade-related behavior and show that the DLPFC is a key node in arousal circuitry and in sensorimotor decisions.

 

Virtual reality (VR) offers the potential to study brain function in complex, ecologically realistic environments. However, the additional degrees of freedom make analysis more challenging, particularly with respect to evoked neural responses. In this paper we designed a target detection task in VR where we varied the visual angle of targets as subjects moved through a three dimensional maze. We investigated how the latency and shape of the classic P300 evoked response varied as a function of locking the electroencephalogram data to the target image onset, the target-saccade intersection, and the first fixation on the target. We found, as expected, a systematic shift in the timing of the evoked responses as a function of the type of response locking, as well as a difference in the shape of the waveforms. Interestingly, single-trial analysis showed that the peak discriminability of the evoked responses does not differ between image locked and saccade locked analysis, though it decreases significantly when fixation locked. These results suggest that there is a spread in the perception of visual information in VR environments across time and visual space. Our results point to the importance of considering how information may be perceived in naturalistic environments, specifically those that have more complexity and higher degrees of freedom than in traditional laboratory paradigms. 

Our state of arousal can significantly affect our ability to make optimal decisions, judgments, and actions in real-world dynamic environments. The Yerkes–Dodson law, which posits an inverse-U relationship between arousal and task performance, suggests that there is a state of arousal that is optimal for behavioral performance in a given task. Here we show that we can use online neurofeedback to shift an individual’s arousal from the right side of the Yerkes–Dodson curve to the left toward a state of improved performance. Specifically, we use a brain–computer interface (BCI) that uses information in the EEG to generate a neurofeedback signal that dynamically adjusts an individual’s arousal state when they are engaged in a boundary-avoidance task (BAT). The BAT is a demanding sensory-motor task paradigm that we implement as an aerial navigation task in virtual reality and which creates cognitive conditions that escalate arousal and quickly results in task failure (e.g., missing or crashing into the boundary). We demonstrate that task performance, measured as time and distance over which the subject can navigate before failure, is significantly increased when veridical neurofeedback is provided. Simultaneous measurements of pupil dilation and heart-rate variability show that the neurofeedback indeed reduces arousal. Our work demonstrates a BCI system that uses online neurofeedback to shift arousal state and increase task performance in accordance with the Yerkes–Dodson law.