# Monitoring Brain State and Behavioral Performance during Repetitive Visual Stimulation
## 4 Discussion
As a result, the mean firing rate decreases due to stimulus repetition [34]. An alternative explanation is that stimulus repetition reduces response in the time domain [33]. According to this theory, a neural network that processes sensory information responds faster to a repetitive stimulus than to a new stimulus, i.e., a stable response. The network connections involved in the response creation were reinforced by the previous presentation of the same stimulus [35]. The NA affects the neuronal response in the occipital [36], parietal [37], and frontal [38] cortex areas in single-unit data, and on the sensory level. As known from the literature, NA, as a rule, reduces the stimulus-related EEG/ECoG response to stimuli. Here, we did not consider the post-stimulus EEG power and did not report such signs of NA. At the same time, an increase in the prestimulus EEG power may reflect the preactivation of sensory neurons. We suppose that in this preparatory state the neural ensemble exhibits less activation in response to the stimulus. Further research should verify this hypothesis by examining the post-stimulus activity as a function of the time-on-task. Another potential explanation is the vital role of alpha-band oscillations for visual perception. Our results showed that an increase in the 9-11-Hz band power correlates with enhancing processing performance. In contrast, many studies have reported negative effects of alpha power on processing performance. For example, the authors of Ref. [12] reported that moderate to low alpha signal strength in the prestimulus period is beneficial for sensory perception. In contrast, a recent review [39] emphasizes that high alpha power facilitates perception by suppressing irrelevant input and generating predictors in the visual cortex. The latter was confirmed by observing an increase in the prestimulus alpha power when participants could predict the identity of the forthcoming stimulus [40]. Finally, the role of alpha-band oscillations depends largely on their incident brain region. For instance, the authors of Ref. [41] provided evidence that right temporal alpha oscillations play a crucial role in inhibiting habitual thinking modes, thereby developing creative cognition. Another work showed that observing a Necker cube can improve subsequent creative problem-solving [42]. In line with these works, we supposed that increasing 9-11 Hz power in the right temporal region reflects a developing ability to inhibit obvious associations. According to Ref. [42], the latter may be a biomarker of neural processes facilitating creative problem-solving. We hypothesize that NA affects the bottom-up processing, while the other two represent the top-down processing components [43; 44]. Assuming that NA facilitates the bottom-up processing, we provide a possible explanation for the negative correlation between the prestimulus EEG power and the decision time to LA stimuli. The morphology of inner edges unambiguously determines the orientation of the LA stimulus. Consequently,during the LA stimulus processing, the bottom-up component prevails [45]. If so, neurons in sensory areas receive the information needed to make the right decision. Repeated stimulation pre-activates these sensory neurons, which leads to a decrease in decision time to LA stimuli. In contrast, the HA stimuli processing may require top-down mechanisms, because the morphology remains similar for the cube being either left or right-oriented [20]. To explain the lack of correlation between the prestimulus EEG power and decision time to HA stimuli, we also assume that during HA processing, the top-down component predominates for most of the time interval, while the bottom-up one may be limited to an earlier and shorter time window [16]. Thus, by facilitating the bottom-up processing, NA has little or no effect on the overall decision time for HA stimuli.