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.
more » « less- NSF-PAR ID:
- 10087953
- Publisher / Repository:
- Proceedings of the National Academy of Sciences
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 116
- Issue:
- 13
- ISSN:
- 0027-8424
- Page Range / eLocation ID:
- p. 6482-6490
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Abstract Objective . When multitasking, we must dynamically reorient our attention between different tasks. Attention reorienting is thought to arise through interactions of physiological arousal and brain-wide network dynamics. In this study, we investigated the relationship between pupil-linked arousal and electroencephalography (EEG) brain dynamics in a multitask driving paradigm conducted in virtual reality. We hypothesized that there would be an interaction between arousal and EEG dynamics and that this interaction would correlate with multitasking performance.Approach . We collected EEG and eye tracking data while subjects drove a motorcycle through a simulated city environment, with the instructions to count the number of target images they observed while avoiding crashing into a lead vehicle. The paradigm required the subjects to continuously reorient their attention between the two tasks. Subjects performed the paradigm under two conditions, one more difficult than the other.Main results . We found that task difficulty did not strongly correlate with pupil-linked arousal, and overall task performance increased as arousal level increased. A single-trial analysis revealed several interesting relationships between pupil-linked arousal and task-relevant EEG dynamics. Employing exact low-resolution electromagnetic tomography, we found that higher pupil-linked arousal led to greater EEG oscillatory activity, especially in regions associated with the dorsal attention network and ventral attention network (VAN). Consistent with our hypothesis, we found a relationship between EEG functional connectivity and pupil-linked arousal as a function of multitasking performance. Specifically, we found decreased functional connectivity between regions in the salience network (SN) and the VAN as pupil-linked arousal increased, suggesting that improved multitasking performance at high arousal levels may be due to a down-regulation in coupling between the VAN and the SN. Our results suggest that when multitasking, our brain rebalances arousal-based reorienting so that individual task demands can be met without prematurely reorienting to competing tasks. -
Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c = 1-10 for activity-specific BCI applications and a moderate range of for the benchmark dataset and general BCI applications.more » « less
-
Mobile edge computing (MEC) is an emerging paradigm that integrates computing resources in wireless access networks to process computational tasks in close proximity to mobile users with low latency. In this paper, we propose an online double deep Q networks (DDQN) based learning scheme for task assignment in dynamic MEC networks, which enables multiple distributed edge nodes and a cloud data center to jointly process user tasks to achieve optimal long-term quality of service (QoS). The proposed scheme captures a wide range of dynamic network parameters including non-stationary node computing capabilities, network delay statistics, and task arrivals. It learns the optimal task assignment policy with no assumption on the knowledge of the underlying dynamics. In addition, the proposed algorithm accounts for both performance and complexity, and addresses the state and action space explosion problem in conventional Q learning. The evaluation results show that the proposed DDQN-based task assignment scheme significantly improves the QoS performance, compared to the existing schemes that do not consider the effects of network dynamics on the expected long-term rewards, while scaling reasonably well as the network size increases.more » « less
-
Spatio-temporal incident prediction is a central issue in law enforcement, with applications in fighting crimes like poaching, human trafficking, illegal fishing, burglaries and smuggling. However, state of the art approaches fail to account for evasion in response to predictive models, a common form of which is spatial shift in incident occurrence. We present a general approach for incident forecasting that is robust to spatial shifts. We propose two techniques for solving the resulting robust optimization problem: first, a constraint generation method guaranteed to yield an optimal solution, and second, a more scalable gradientbased approach. We then apply these techniques to both discrete-time and continuoustime robust incident forecasting. We evaluate our algorithms on two different real-world datasets, demonstrating that our approach is significantlymore » « less
-
Changes in behavioral state, such as arousal and movements, strongly affect neural activity in sensory areas, and can be modeled as long-range projections regulating the mean and variance of baseline input currents. What are the computational benefits of these baseline modulations? We investigate this question within a brain-inspired framework for reservoir computing, where we vary the quenched baseline inputs to a recurrent neural network with random couplings. We found that baseline modulations control the dynamical phase of the reservoir network, unlocking a vast repertoire of network phases. We uncovered a number of bistable phases exhibiting the simultaneous coexistence of fixed points and chaos, of two fixed points, and of weak and strong chaos. We identified several phenomena, including noise-driven enhancement of chaos and ergodicity breaking; neural hysteresis, whereby transitions across a phase boundary retain the memory of the preceding phase. In each bistable phase, the reservoir performs a different binary decision-making task. Fast switching between different tasks can be controlled by adjusting the baseline input mean and variance. Moreover, we found that the reservoir network achieves optimal memory performance at any first-order phase boundary. In summary, baseline control enables multitasking without any optimization of the network couplings, opening directions for brain-inspired artificial intelligence and providing an interpretation for the ubiquitously observed behavioral modulations of cortical activity.