The effects of multi-colour light filtering glasses on human brain wave activity.

The prevalence of electronic screens in modern society has significantly increased our exposure to high-energy blue and violet light wavelengths. Accumulating evidence links this exposure to adverse visual and cognitive effects and sleep disturbances. To mitigate these effects, the optical industry has introduced a variety of filtering glasses. However, the scientific validation of these glasses has often been based on subjective reports and a narrow range of objective measures, casting doubt on their true efficacy. In this study, we used electroencephalography (EEG) to record brain wave activity to evaluate the effects of glasses that filter multiple wavelengths (blue, violet, indigo, and green) on human brain activity. Our results demonstrate that wearing these multi-colour light filtering glasses significantly reduces beta wave power (13-30 Hz) compared to control or no glasses. Prior research has associated a reduction in beta power with the calming of heightened mental states, such as anxiety. As such, our results suggest that wearing glasses such as the ones used in this study may also positively change mental states, for instance, by promoting relaxation. This investigation is innovative in applying neuroimaging techniques to confirm that light-filtering glasses can induce measurable changes in brain activity.

Validation of a mobile fNIRS device for measuring working memory load in the prefrontal cortex.

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that measures cortical blood flow to infer neural activation. Traditionally limited to laboratory settings due to high costs and complex operation, recent advancements have introduced mobile fNIRS devices, significantly broadening the scope of potential research participants. This study validates the use of the Mendi, a two-channel mobile fNIRS system, for measuring prefrontal oxyhemoglobin concentration changes during an n-back task. We manipulated task difficulty through different n-back levels (one-back versus three-back), revealing increased oxyhemoglobin concentrations in the prefrontal cortex during the more demanding three-back task compared to the one-back task. This finding demonstrates the Mendi's ability to distinguish between low and high cognitive task loads. Behavioural data, showing a decrease in accuracy under high load conditions, further corroborates these neuroimaging findings. Our study validates the Mendi mobile fNIRS system as an effective tool for assessing working memory load and underscores its potential in enhancing neuroscientific research accessibility. The user-friendly and cost-effective nature of mobile fNIRS systems like the Mendi opens up neuroscientific research to a diverse set of participants, enabling the investigation of neural processes in real-world environments across a variety of demographic groups.

How low can you go? Measuring human event-related brain potentials from a two-channel EEG system.

Over the past ten years, there has been a rapid increase in the availability and use of mobile electroencephalography (mEEG) in research. Indeed, researchers using mEEG have recorded EEG and event-related brain potentials in a wide range of environments - for example, while walking (Debener et al., 2012), riding a bike (Scanlon et al., 2020), or even in a shopping mall (Krigolson et al., 2021). However, given that low-cost, ease-of-use, and setup speed provide the primary advantages of an mEEG system over large array traditional EEG systems, an important and unresolved question is just how many electrodes does an mEEG system need to collect research-quality EEG data? Here, we tested whether or not a two-channel forehead-mounted mEEG system - the "Patch" - could measure event-related brain potentials within their established amplitude and latency characteristics (Luck, 2014). In the present study, participants performed a visual oddball task while we recorded EEG data from the Patch. Our results demonstrated that we could capture and quantify the N200 and P300 event-related brain potential components using a minimal electrode array forehead-mounted EEG system. Our data further support the idea that mEEG can be used for quick and rapid EEG-based assessments, such as measuring the impact of concussions on the sports field (Fickling et al., 2021) or assessing the impact of stroke severity in a hospital (Wilkinson et al., 2020).

Exercising is good for the brain but exercising outside is potentially better.

It is well known that exercise increases cognitive function. However, the environment in which the exercise is performed may be just as important as the exercise itself. Time spent in natural outdoor environments has been found to lead to increases in cognition similar to those resulting from acute exercise. Therefore, the benefits of both exercise and nature exposure suggest an additive impact on brain function when both factors are combined. This raises the question: what is the interaction between acute exercise and environment on cognition? We answered this question using electroencephalography to probe cognitive function using the oddball task before and after brief indoor and outdoor walks on 30 participants (average 21 years old, 95% CI [20, 22]). Our results demonstrate improved performance and an increase in the amplitude of the P300, an event-related neural response commonly associated with attention and working memory, following a 15-min walk outside; a result not seen following a 15-min walk inside. Importantly, this finding indicates that the environment may play a more substantial role in increasing cognitive function such as attention than exercise, at least in terms of acute exercise (i.e., a brief walk). With the world's growing urbanization and the associated increase in sedentary time indoors, a deeper understanding of how these factors interact and influence cognition may be critical to combat adverse health effects.

Feasibility of the optimal cerebral perfusion pressure value identification without a delay that is too long.

Optimal cerebral perfusion pressure (CPPopt)-targeted treatment of traumatic brain injury (TBI) patients requires 2-8 h multi-modal monitoring data accumulation to identify CPPopt value for individual patient. Minimizing the time required for monitoring data accumulation is needed to improve the efficacy of CPPopt-targeted therapy. A retrospective analysis of multimodal physiological monitoring data from 87 severe TBI patients was performed by separately representing cerebrovascular autoregulation (CA) indices in relation to CPP, arterial blood pressure (ABP), and intracranial pressure (ICP) to improve the existing CPPopt identification algorithms. Machine learning (ML)-based algorithms were developed for automatic identification of informative data segments that were used for reliable CPPopt, ABPopt, ICPopt and the lower/upper limits of CA (LLCA/ULCA) identification. The reference datasets of the informative data segments and, artifact-distorted segments, and the datasets of different clinical situations were used for training the ML-based algorithms, allowing us to choose the appropriate individualized CPP-, ABP- or ICP-guided management for 79% of the full monitoring time for the studied population. The developed ML-based algorithms allow us to recognize informative physiological ABP/ICP variations within 24 min intervals with an accuracy up to 79% (compared to the initial accuracy of 74%) and use these segments for timely optimal value identification or CA limits determination in CPP, ABP or ICP data. Prospective clinical studies are needed to prove the efficiency of the developed algorithms.