**1. Introduction**

Sensory processing is a fundamental brain function that allows us to more easily interact with each other and with our environment. In everyday life, we collect sensory data and process it for interpretation and decision making [1]. The accuracy and timeliness of our decisions depend on the speed and correctness of sensory processing. The effectiveness of sensory processing, in turn, is determined by a number of exogenous and endogenous factors [2]. In particular, the exogenous component reflects the quality of the sensory input. Thus, when faced with unambiguous information, we can easily interpret it. On the contrary, when information becomes ambiguous, interpreting it takes more effort.

In turn, the endogenous component depends on the state of the person; on their attention, fatigue, and subjective experience [3]. In many experimental studies where ambiguous stimuli were used, endogenous effects were found to be especially pronounced

**Citation:** Kuc, A.K.; Kurkin, S.A.; Maksimenko, V.A.; Pisarchik, A.N.; Hramov, A.E. Monitoring Brain State and Behavioral Performance during Repetitive Visual Stimulation. *Appl. Sci.* **2021**, *11*, 11544. https://doi.org/10.3390/app112311544

Academic Editor: Fabio La Foresta

Received: 10 October 2021 Accepted: 30 November 2021 Published: 6 December 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

when the sensory information quality was low [4]. Therefore, the observer must concentrate to gather more information to make the right decision, relying on personal experience to extrapolate limited information or unresolve its ambiguity.

The conditions under which the observer receives and processes information are important as well. For example, high-speed driving on a rainy night requires the very fast processing of low-quality information. Performing monotonous tasks with increased responsibility (e.g., flight or power plant operators) also requires maintaining high performance and emergence preparedness. In these stressful conditions, the influence of exogenous and endogenous factors on the likelihood of perceptual errors should be considered. Therefore, knowing and monitoring these factors can help predict perceptual errors and reduce their probability. Furthermore, human condition monitoring (endogenous factor) is a task for passive brain–computer interfaces (BCIs) [5]. Unlike the traditional active BCI, which issues control commands through mental intent, passive BCIs continuously monitor the brain state during extended periods of cognitive activity and signal if it deviates from the normal state [6].

To control the quality of received information and its processing, the BCI must track both exogenous and endogenous components. Thus, the BCI must meet the task requirements (exogenous factor) along with the neural activity (endogenous factor). Moreover, objective assessments of the task requirements may depend on the amount of information, its ambiguity, and multimodality. Subjective estimates can be derived from the observer's reaction, such as response time, eye movements, and other behavioral indicators [7]. To take into account all these processes, it is necessary to move from a passive to a reactive BCI. The latter uses stimuli and analyzes the brain state through time intervals assigned to them [8]. Following this concept, a reactive BCI should analyze the brain state while performing a task. This will provide information on the influence of endogenous and exogenous factors on the speed and quality of information processing.

The further development of BCIs aims not only at the detection, but also the prediction of the human states. These BCIs will give rise to the artificial intelligence systems that assist or alarm when detecting a high probability of critical errors. Developing such systems requires finding the associations between the current state of the BCI operator and their performance in solving ongoing tasks. A bulk of literature associates changes in the human condition with their behavioral performance in ongoing tasks. In particular, attention, a fundamental aspect of the observer's state, modulates prestimulus alpha- and beta-band power [9–11], influencing the accuracy of perceptual decisions. Thus, either medium or low alpha- and high beta-band power during the prestimulus period is beneficial for sensory perception [11,12]. According to [13], the power and the prestimulus EEG phase coupling in the alpha- and beta-bands affect visual perception performance. Namely, better performance is associated with low phase coupling in the alpha-band and high phase coupling in the beta-band. Recent work [14] revealed that EEG power in the beta-2 frequency band at rest negatively correlated with the response times in the ongoing attentional task. While most works reported correlations between the neural correlates averaged across the trials, or between event-related potentials, in recent work [15], the authors used EEG power in different bands to predict individual performance in single trials contributing to the BCI problem.

Complementing the existing literature, we examined how the prestimulus EEG power predicts behavioral performance depending on the task demands. We considered a longlasting monotonous experiment in which the participant perceived ambiguous stimuli and reported on each stimulus interpretation with the joystick buttons. The visual stimulus was an ambiguous Necker cube. The inner edges contrast defines one of two possible cube's orientations, left or right, and determines stimulus ambiguity. When ambiguity is low, cubes morphology is different for the left and right orientations. Therefore, subjects easily report the correct one. For the high ambiguity, stimulus morphology becomes similar for different orientations; therefore, subjects spent more effect to find the differences. In the recent works, we observed that subjects responded faster to the Necker cubes presented

at the end of the experiment [16]. We also found that the brain utilized different neural mechanisms when processing stimuli with low and high ambiguity [2,4,17]. Based on these results, we hypothesized that during a long experiment with the Necker cubes, the observer's state changed, causing changes in behavioral performance. We expected to find the neural correlates of these changes in the prestimulus state and use them to predict the performance of the ongoing stimulus. We also supposed that changes in the human condition had a different effect depending on the stimulus ambiguity.

To test this hypothesis, we tracked the behavioral characteristics (decision times and errors) and simultaneously detected the EEG signals during a long monotonous task, including the Necker cubes interpretations. Behavioral monitoring revealed that decision time decreased with time on task, despite the ambiguity. For high ambiguity, we also observed a reduction of perceptual errors. EEG analysis showed growing prestimulus 9–11 Hz EEG power in the right temporal region. This EEG power negatively correlated with the decision time to the stimuli, with low ambiguity and the erroneous responses rate to stimuli with high ambiguity. The obtained results confirm that monitoring prestimulus EEG power enables predicting perceptual performance on the behavioral level.

## **2. Materials and Methods**

## *2.1. Participants*

Twenty healthy volunteers (nine females, 26–35 y.o.) with normal or corrected-tonormal vision participated in the experiments after providing written informed consent. Participants took part in similar experiments not earlier than six months before. All experiments were carried out in accordance with the requirements of the Declaration of Helsinki and approved by the local Research Ethics Committee of the Innopolis University.
