Regulation of arousal via online neurofeedback improves human performance in a demanding sensory-motor task

Abstract

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.

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

“…Motor variability due to variability in human kinematic parameters, e.g., force field adaptation, speed and trajectory, and motivational factors such as level of user engagement, arousal and feelings of competence, necessary for performing a motor task is an integral part of the motor learning process (Duarte and Reinkensmeyer, 2015;Úbeda et al, 2015;Edelman et al, 2019;Faller et al, 2019). Such variability does not necessarily represent noise contents only, but may potentially be a manifestation of motor and perceptual learning processes.…”

“…Recent studies showed that BCI skill acquisition and associated physiological changes may improve BCI performance in both patients and healthy users (Perdikis et al, 2018;Edelman et al, 2019). Complex or cognitively entertaining tasks that require greater user engagement or motivation can compensate for intra-and inter-subject variability, leading to enhanced BCI learning in adverse operating conditions (Perdikis et al, 2018;Edelman et al, 2019;Faller et al, 2019;Li et al, 2019).…”

A hidden Markov model for analyzing eye-tracking of moving objects

“…A number of innovative recent papers have utilized eye-tracking to provide real-time feedback to humans as they perform certain tasks, in contexts such as visual search (Drew and Williams, 2017), manual assembly (Renner and Pfeiffer, 2017), and medical (Ashraf et al, 2018) or programming (Sun and Hsu, 2019) education. Eye-tracking-based feedback has potential to be faster, cheaper, and more widely usable than similar feedback based on neural data collected as participants perform tasks in an fMRI (Awh and Vogel, 2015;Faller et al, 2019), given the huge expense and practical constraints associated with fMRI, as well as the relatively slow timecourse of the BOLD signal. For these and other applications, it may be desirable to adapt our proposed model to the online setting, in which object-tracking predictions must be made rapidly and using only data from previous (as opposed to future) timepoints to inform the current prediction.…”

Efficacy, Trainability, and Neuroplasticity of SMR vs. Alpha Rhythm Shooting Performance Neurofeedback Training

“…The third focus of our research was to study the effects of SP-NFT on neuroplasticity, that is, whether SP-NFT can change the brain neural activity and whether the brain activity of the participant has changed throughout a period of training (Ghaziri et al, 2013;Megumi et al, 2015;Faller et al, 2019). In this study, we tested this by examining the resting EEG of the participants before and after NFT.…”