Scattering Improves Temporal Resolution of Vision: A Pilot Study on Brain Activity

Abstract

Temporal vision is a vital aspect of human perception, encompassing the ability to detect changes in light and motion over time. Optical scattering, or straylight, influences temporal visual acuity and the critical flicker fusion (CFF) threshold, with potential implications for cognitive visual processing. This study investigates how scattering affects CFF using an Arduino-based psychophysical device and electroencephalogram (EEG) recordings to analyze brain activity during CFF tasks under scattering-induced effects. A cohort of 30 participants was tested under conditions of induced scattering to determine its effect on temporal vision. Findings indicate a significant enhancement in temporal resolution under scattering conditions, suggesting that scattering may modulate the temporal aspects of visual perception, potentially by altering neural activity at the temporal and frontal brain lobes. A compensation mechanism is proposed to explain neural adaptations to scattering based on reduced electrical activity in the visual cortex and increased wave oscillations in the temporal lobe. Finally, the combination of the Arduino-based flicker visual stimulator and EEG revealed the excitatory/inhibitory stimulation capabilities of the high-frequency beta oscillation based on the alternation of an achromatic and a chromatic stimulus displayed in the CFF.

1. Introduction

Temporal vision is the ability of human visual processing to perceive and interpret visual stimuli over time. It encompasses the detection of flicker and changes in luminance and motion [1], which are crucial for tasks such as tracking moving objects and perceiving dynamic scenes. Compared to studies on spatial vision, temporal aspects of vision have received relatively little attention in recent decades in both psychophysical and optical experiments [2]. This is probably because the main effort in visual optics has traditionally focused on central vision [3], while temporal processing mechanisms are related to the peripheral retinal field [4].

Temporal aspects of vision are usually evaluated by psychophysical methods such as critical flicker fusion frequency [5], and temporal contrast sensitivity (TCS) functions [6]. These techniques assess the sensitivity to changes in contrast and motion (or flicker) as a function of temporal frequency, revealing information about magno and parvocellular pathways [7,8]. Clinically, the assessment of temporal vision by psychophysical approaches is a valuable tool for detecting and tracking visual and neurological disorders. For instance, flicker perimetry allows the detection of functional loss in the magnocellular pathway in glaucoma patients [9]. Similarly, reduced TCS functions are observed in Parkinson’s [10] and Alzheimer’s disease [11] patients who present with both retinal and cortical pathways affected.

In addition to psychophysical approaches, electrophysiological methods can help to understand the temporal vision basis at a neural level. Electroretinography (ERG) measures the electrical activity of the retina as a response to a light stimulus [12]. In particular, electroretinogram flicker photometry has been employed to explore cone photoreceptor density [13], to search for abnormalities in retinal function, or to establish the risk of developing retinopathy [14].

However, motion processing occurs in the visual cortex [15]. The first encoding starts from direction-selective neurons in the primary visual cortex (V1). These inputs are received from the middle temporal (MT) area specialized for the processing of image motion [16] and project to the medial superior temporal area (MST) for complex motion properties processing such as optic flow [17]. Therefore, beyond the electrical response at the retina, electroencephalography (EEG) records the electrical activity [18] and then can provide invaluable information about temporal processing [19] in the visual cortex, which shows electrical activity in the same frequency band at which the retina is stimulated [20].

In that sense, the study of visual evoked potentials (VEPs) in response to a flicker of visual stimuli [21] through EEG allows us to study the brain’s response to visual perception. From the first reported VEPs measured in 1934 by Adrian and Matthews [22,23], the interpretation of potential brain waves in the cortex has been extensively reported in vision research. The study of VEPs has been reported as a function of the contrast of the visual pattern [24], visual acuity [25], spatiotemporal aspects of vision [26], or color coding [27].

Considering that VEPs are the electric activity of the whole visual pathway (i.e., from the optic nerve towards the primary visual cortex) [28], these potentials allow the exploration of the visual system response through neuronal activity independent of the attention of the patient [29]. This is particularly valuable in those cases in which vision is in conditions of perceptual degradation (for example, intraocular opacifications, optical aberrations, or the presence of straylight), and the patient’s cognitive response may be confusing in purely psychophysical visual tests. While many efforts have been put into studying the impact of those factors that degrade ocular optical quality on visual-spatial resolution [30,31,32,33,34,35], the literature focused on studying these effects on the temporal aspects of vision is scarce.

In addition to optical aberrations, ocular straylight can diminish visual acuity and spatial contrast sensitivity, especially under conditions of low illumination or in the presence of glare. Focusing on brain–computer interfaces (BCIs), we recently reported an Arduino-powered device for the psychophysical measurement of chromatic critical frequency fusion (CFF) [36]. It was found that the presence of high-order aberrations improves the temporal resolution. Then, if an inhibitory interaction between the parvo and magnocellular pathways is considered [37], spatial and temporal resolution are antagonistic in visual performance. Based on this last reasoning, the main hypothesis is that the presence of straylight improves visual temporal resolution.

This article aims to explore the influence of straylight on chromatic critical frequency fusion (CFF) in young and healthy adult subjects. In addition, a pilot study is introduced by recording EEGs to provide insights into how the brain processes temporal visual information and how it is influenced by optical factors like straylight. By examining the concepts of critical frequency fusion, straylight, and electroencephalography in conjunction, we hope to advance the understanding of how light dynamics influence our visual experience and the neural mechanisms underlying temporal vision.

2. Materials and Methods

2.1. Subjects

A sample of healthy adult volunteers (N = 30, age range 21 ± 2 years old) participated in the study. All subjects had normal or corrected-to-normal vision and no history of neurological or ophthalmological conditions. Informed consent was obtained in accordance with ethical standards; the study was conducted in accordance with the Declaration of Helsinki.

2.2. Critical Flicker Fusion Frequency and EEG Assessment

CFF was assessed using a previously reported Arduino-based portable device and using the same protocol as described in reference [36]. Straylight was induced by introducing Bangerter foils (Ryser Optik AG, St. Gallen, Switzerland) to simulate real-world light scattering effects as described in [37]. In particular, translucent Bangerter foils of grading 0.8 were chosen; this density of foil is expected to bring the Snellen Visual Acuity down to 20/25 according to the manufacturer’s technical specifications.

EEG measurements were collected from one aleatory subject for the sample of 10 different visual stimuli conditions (described in Table 1) in a binocularly light-adapted room with ambient light of 50 Lux. The recording time was 5 s for each visual condition in which the volunteer was instructed to avoid blinking, saccades, or eccentric fixations, which could introduce artifacts into the EEG signal.

The subject at all times remained fixed to the visual condition at the beginning and disconnection of the recording of EEG signals. The first and last seconds of the EEG signal were discarded to avoid reactions to the subject’s attention to the measurement instructions. The measurements were carried out in the Visual Optics laboratory at the University of Zaragoza (Spain).

2.3. Electroencephalogram Device

A portable and wireless water-based EEG device was used for the pilot study (Versatile 8, BitBrain Technologies, Zaragoza, Spain). The semi-dry (tap water humidity) 8-electrode array can be configured following the international 10–20 system; the chosen configuration is shown in Figure 1. The sensor’s signals are wireless and acquired by an amplifier with an integrated accelerometer, gyroscope, and magnetometer sensor, whose technical specifications are summarized in Table 2.

The electrode positions were placed to analyze the brain activity at the occipital (channels ch#1 and ch#2), posterior temporal (ch#3 and ch#4), frontal (ch#5 and ch#6), and pre-frontal (ch#7 and ch#8) lobes (see Figure 1).

2.3.1. EEG Signal Processing

A custom-written Matlab script was employed for EEG signal processing. This sub-section described a step-by-step processing of the raw EEG signals as follows:

Signal amplitude adjustment: The raw EEG signal is loaded and adjusted to millivolts (mV) by dividing the original signal by the amplification gain (G):

(1)$EE{G}_{mv}={\displaystyle \frac{EEG}{G}}$

Time vector construction: The EEG device provides a sampling frequency (F_s) of 256 Hz; the time vector t is calculated as follows:

(2)$T_s={\displaystyle \frac{1}{F_s}} , t\left[n\right]=n·T_s, n=1,2,\dots , N$

where N is the number of data points of the EEG signal.

Signal normalization: The normalized EEG signal (EEG_final) is computed as follows:

(3)$EE{G}_{final}={\displaystyle \frac{EE{G}_{mV}-\mu }{\sigma }}$

where µ and σ are the mean and the standard deviation, respectively.

Band-pass filtering: A 4th-order Butterworth band-pass filter with a frequency range of 0.5–40 Hz is applied. The normalized cutoff frequencies are as follows:

(4)${\omega }_{low}={\displaystyle \frac{f_{low}}{F_s/2}}, {\omega }_{high}={\displaystyle \frac{f_{high}}{F_s/2}}$

The filtered signal (EEG_filt) is obtained using the zero-phase filtering Matlab function “filtfilt”.

Power spectra density: Once the EEG signal is filtered, the fast Fourier transform (FFT) is calculated:

(5)$FFT\left(f\right)={\displaystyle \frac{1}{N}}{{\displaystyle \sum }}_{n=1}^{N}EE{G}_{filt}\left[n\right]·e^{-j2\pi fn}$

The one-sided Power Spectral Density (PSD) is then calculated:

(6)$PSD\left(f\right)=2·{\left|FFT\left(f\right)\right|}^2$

Frequency bands and averaged band power: For standard EEG frequency bands (delta: 0.5–4 Hz, theta: 4–8 Hz, alpha: 8–13 Hz, beta: 13–30 Hz, and gamma: 30–40 Hz), the mean averaged band power (ABP) is computed as follows:

(7)$ABP={\displaystyle \frac{1}{K}}{{\displaystyle \sum }}_{k ϵ band}PSD\left[k\right]$

where K is the number of frequency bins in the band.

Continuous wavelet transform: The continuous wavelet transform (CWT) has been widely used in neuroscience to investigate cognitive processes and neurological disorders [38,39]. The CWT is defined as follows:

(8)$CWT\left(a,b\right)={{\displaystyle \int }}_{-\infty }^{\infty }EE{G}_{final}\left(t\right)·{\psi }_{a,b}^{*}\left(t\right)dt$

where ${\psi }_{a,b}\left(t\right)$ is the scaled and translated wavelet function:

(9)${\psi }_{a,b}\left(t\right)={\displaystyle \frac{1}{\sqrt{a}}}\psi \left({\displaystyle \frac{t-b}{a}}\right),$

with a representing the scale (inversely related to frequency), b the translation, and ψ(t) the mother wavelet. The Morlet wavelet is employed here due to its effectiveness in time-frequency analysis of oscillatory signals, as it combines a sinusoidal oscillation with a Gaussian envelope of width σ:

(10)$\psi \left(t\right)=e^{i{\omega }_{0}t}{e}^{-{\displaystyle \frac{t^2}{2{\sigma }^2}}},$

The computation yields coefficients wt(f.t) and their corresponding frequencies f_wt. Then, the wavelet transform coefficients are filtered to retain only frequencies within the range of interest [0.5–40 Hz]:

(11)$w{t}_{filt}\left(f,t\right)=wt\left(f\le 40,t\right)$

Finally, the time-frequency visualization is shown by creating a time-frequency map computed as the magnitude of the filtered wavelet coefficients. This map provides a visual representation of the energy distribution across time and frequency, highlighting dynamic EEG features.

(12)$Map\left(t,f\right)=\left|w{t}_{filt}\left(f,t\right)\right|$

2.3.2. Topographic Heatmap Generation from EEG Signals

Topographic EEG heatmaps were generated using a custom-written Matlab script as follows:

Channel averaging: The EEGs are averaged over time for each channel to obtain a representative value:

(13)$EE{G}_{mean}\left(c\right)={\displaystyle \frac{1}{N}}{{\displaystyle \sum }}_{n=1}^{N}EE{G}_{filt}\left(n,c\right)$

where n and c are the sample index and channel index, respectively.

To create a continuous topographic map, the electrode positions (x,y) are assigned based on the standard 10–20 EEG system or, in our case, according to the configuration shown in Figure 1. Then, a 2D interpolation function is applied over a dense grid:

(14)$z_q=GridData\left(x,y,EE{G}_{mean}, x_q,y_q,\prime v4\prime \right)$

where x_q and y_q are the grid coordinates and ‘v4’ refers to a biharmonic spline interpolation for smoothness. Then, a mask is applied to restrict the map to an elliptical shape representing the EEG scalp:

(15)$Mask\left(x_q,y_q\right)=\{\begin{array}{c}1, {\displaystyle \frac{x_q^2}{a^2}}+{\displaystyle \frac{y_q^2}{b^2}}\le 1\\ NaN, otherwise.\end{array}$

where a and b are the semi-major and semi-minor axes of the ellipse approximating the head shape.

Finally, the topographic map is visualized using a color-coded contour plot:

(16)$Topographic Map \left(x_q,y_q\right)=Contourf\left(x_q,y_q,z_q, levels\right)$

where ‘levels’ determines the granularity of contours. The electrode locations are overlaid, and a head outline is drawn for anatomical reference.

2.4. Data and Statistical Analysis

CFF values were obtained for chromatic and achromatic stimuli as described in [36]. Chromatic stimuli were presented for red, green, and blue lights, and achromatic stimulus consisted of the white fusion of perceived red, green, and blue flicker lights at the critical fusion frequency. Measurements were acquired binocularly for each subject in normal vision conditions, and the EEG scalp and Bangerter foils were worn on the frame glasses with neutral refractive power. Once the CFF was reached and then the subject perceived a steady stimulus, EEG data were collected with a sampling rate of 256 Hz for 10 s. Data were preprocessed to remove artifacts and analyzed for changes in neural oscillations corresponding to temporal visual processing. Frequency bands of interest were analyzed to observe modulation in response to straylight. Statistical comparisons were made using a one-way repeated-measures analysis of variance (one-way RM ANOVA) to assess the effect of straylight on CFF thresholds and EEG band power analysis. Statistical analyses and graphical representations were performed using Sigmaplot 12.0 scientific software.

3. Results

3.1. Influence of Induced Scattering on Chromatic Critical Frequency Fusion Frequency

Figure 2 shows the mean CFF values obtained from a sample of 30 young adult subjects, binocularly for the three chromatic stimuli [red (Figure 2a), green (Figure 2b), and blue (Figure 2c)] and for an achromatic (Figure 2d) stimulus (i.e., white light) in normal viewing conditions. After the control CFF measurements (‘Normal vision’ bars in Figure 2), the subjects were asked to wear a glasses frame with neutral refractive power to which a Bangerter foil was attached; then, the CFF was measured again, obtaining those represented values as ‘Scattering’ in Figure 2. For both chromatic and achromatic stimuli, the found CFF values were significantly larger as a consequence of forward-induced scattering by the Bangerter foils. On average, there was an increase in CFF values of 10.47%, 10.42%, and 9.15% for red, green, and blue stimuli and 10.03% for achromatic flickering. Larger CCF values imply enhanced temporal resolution as a consequence of induced scattering, which conversely degrades spatial resolution and optical quality of the retinal image.

3.2. Influence of Induced Scattering in EEG Signals During Polychromatic and Achromatic Retinal Stimulation at the CFF

Figure 3 compares the raw EEG signals from a volunteer for each channel in normal vision conditions (‘control’ column), wearing a Bangerter filter (‘Scattering’ column) while the subject is seeing a fixation point composed of a fixed white LED stimulus. For each channel, the upper and lower dashed lines represent an amplitude of ±0.3 mV, respectively. For better visualization, only one second of recording is shown for each visual condition. A visual inspection reveals altered signal patterns in channels ch#1, ch#2, ch#3, ch#4, and ch#6. While channels ch#1 and ch#2 measure the brain activity in the visual cortex, channels #3 and #4 measure the activity at the posterior temporal and frontal right (ch#6) lobes.

Once the electrical activity was pre-processed for each condition and electrode, the EEG amplitude of the eight channels was averaged and filtered to isolate the waveforms corresponding to delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–40 Hz) brain waves. Figure 4 compares the 8-channel averaged EEG signal amplitude for each wave band as a function of the flicker stimulus. The control column corresponds to the EEG signals acquired for a steady achromatic visual stimulus. Figure 5 compares the band-filtered EEGs under scattering-induced visual conditions. It can be observed that the waveforms not only show different patterns for different chromatic flicker stimuli but also differ under scattering-induced visual conditions.

To quantify chosen alterations in the EEG patterns, Figure 6 compares the computed averaged band power (ABP) values for both control and chromatic flickering (Figure 6). For natural viewing conditions (i.e., without induced scattering) of the chromatic flicker stimuli, the average band power (ABP) revealed reduced brain activity in the delta band for red, blue, and achromatic stimuli at the CFF, but this reduction was not statistically significant. For red (Figure 6a) and achromatic (Figure 6d) CFF stimuli, the rest of the wave bands (i.e., theta, alpha, beta, and gamma) showed similar ABP values while flickering the retina at the corresponding CFF compared to the control measurements (steady achromatic perception of a punctual stimulus). However, for green (Figure 6b) and blue flicker (Figure 6c) stimuli, a significant reduction in brain activity for delta and alpha bands was found when stimulating the retina with the corresponding chromatic flicker light.

Since the ABP of Figure 6 corresponds to the average band power of all electrode channels, Figure 7 shows the topographic brain activity maps filtered for those wave bands at the flicker conditions in which a significant ABP was found in Figure 6. For green flicker stimulus, the control topographic maps show the main brain activity for delta (Figure 6a), theta (Figure 6b), and alpha (Figure 6c) waves focused on the frontotemporal and prefrontal areas (electrodes T6 and F8 and Fp1 and Fp2, respectively), but after stimulating the retina with flicker CFF green light (Figure 7d–f) a redistribution of delta, theta, and alpha oscillations are observed with quasi-symmetric topographic patterning. Regarding the blue CFF stimulus, theta and alpha wave activity was significantly reduced in the right prefrontal cortex, as shown in Figure 7i,j.

Figure 8 shows the averaged band power (power spectrum density across all channels) for each wave band as a function of the scattering-induced visual conditions (see Table 2). Figure 8a shows how scattering increases delta, theta, and beta activity, but this modulation is not statistically significant. However, there was a statistical increase in gamma activity (p = 0.01) due to induced visual scattering effects only. When flickering the retina at the chromatic CFF, the red flicker stimulus showed an increased brain activity for alpha and beta oscillations (Figure 8c), while for blue CFF, significant increases were found in theta and beta (Figure 8e) waves. For green CFF, the results showed behavior similar to that of the control condition (Figure 8a).

For achromatic stimulus, the influence of scattering showed a reverse behavior by significantly reducing the beta activity (Figure 8b). These results show how flickering the retina at the CFF can modulate the electrical brain activity at both low-amplitude (Figure 7e, blue CFF stimulus) and high-frequency waves (Figure 8b–d). While chromatic CFF stimuli increase the electrical activity, achromatic flickering light reduces beta oscillations.

3.3. Analysis of Electrical Activity During Scattering-Induced Chromatic and Achromatic Retinal Flickering in Major Brain Regions

In previous subsections, results indicated that CFF increased significantly under straylight conditions, implying enhanced temporal resolution. Furthermore, EEG analysis showed increased neural synchrony in fast-frequency bands for chromatic flicker stimuli and decreased beta activity for achromatic flicker stimuli. Figure 9 shows the topographic maps of brain electrical activity for the 8-channel configuration (see Figure 1). A visual inspection revealed main changes in prefrontal and frontotemporal regions when scattering was present, suggesting heightened neural responsiveness to visual flicker under these conditions.

To quantify the changes in the electrical activity shown in Figure 9, the energy change at each channel was computed as the relative changes in energy density due to chromatic and achromatic retinal flickering effects with respect to a steady visual stimulus (Figure 10a–d, first row, blue bars) and due to induced scattering over the flickering stimuli (Figure 10e–h, bottom row, red bars) for each channel and visual stimuli condition. These calculations allow quick inspection, revealing that the main energy changes occur at the T6 channel for non-scattering conditions, while these changes move towards the frontal and prefrontal regions. In particular, the main energy changes in scattering-free chromatic and achromatic CFF stimuli are represented by channel T6, while channel F7 records the main energy changes in retinal stimulation by scattering-induced flickering light.

The next subsections compare the EEG analysis of the wave frequency bands of the control activity with chromatic flicker stimuli and with scattering-induced effects in the same chromatic stimuli. To avoid repetitive results, these analyses were carried out for blue flicker stimulus and using the Morlet Wavelet transform in the time-frequency domain.

3.3.1. Electrical Activity Analysis at the Occipital Lobe

According to the scalp configuration shown in Figure 1, the occipital lobe corresponds to electrodes O1 and O2; this subsection focuses on the analysis of electrode O2. Figure 11 compares the Morlet Wavelet transform for the control (Figure 11a), blue CFF stimulus (Figure 11b), control condition under scattering (Figure 11c), and blue CFF with induced scattering (Figure 11d). While the changes are noticeable and visible under flickering conditions (right panel), Figure 11e,f shows the power spectral density for each condition as a waveband. As shown, at the occipital region, the scattering effects reduce the electrical activity of the brain at all frequency bands, with the exception of gamma oscillation. However, under scattering-induced flickering blue stimulation, the brain activity significantly increases the slow-frequency theta band and drastically reduces theta towards the faster frequency bands. It is important to highlight the pyramidal pattern formed by the decay of neuronal activity from high to low frequencies in the wavelet transform for scattering-induced blue CFF stimuli (Figure 11d).

3.3.2. Electrical Activity Analysis at the Temporal Lobe

The temporal lobe activity was chosen at the electrode T6. Figure 12 shows the corresponding wavelet transform and the PSD values for each waveband and the mentioned visual conditions. In this brain region, the behavior is radically opposite to the visual cortex; the scattering effects in natural viewing conditions drastically increase the electrical activity for slow and fast frequency bands. For the blue flicker stimulus, the main activity is shown for the delta band and is drastically reduced for the rest of the frequency bands after inducing scattering, preserving higher electrical activity in all frequency bands for the scattering flicker condition.

3.3.3. Electrical Activity Analysis at the Frontal Lobe

At the frontal lobe (Figure 13), reduced electrical activity in control and under blue CFF stimulus is observed with respect to the activity under scattering effects, for which the predominant activity is given for delta waves. For scattering-only conditions, the theta and fast-frequency bands are similar; this behavior is exponentially reduced for scattering-induced blue CFF stimulus.

3.3.4. Electrical Activity Analysis at the Pre-Frontal Lobe

Finally, at the prefrontal lobe, the electrical activity is shown to be similar for theta and fast-frequency bands in control (Figure 14a) and scattering-only (Figure 14c) conditions, while the theta activity is significantly reduced in the last-mentioned condition (Figure 14e). However, the pre-frontal lobe shows decreased activity when flickering the retina with scattering-induced flicker blue light (Figure 14d) with respect to the scattering-free retinal flickering (Figure 14b). In all cases, the predominant activity was found for delta waves.

4. Discussion

The critical flicker fusion frequency (CFF) parameter is a good descriptor of the temporal resolution of the visual system and has been widely used in clinical vision sciences research. Decreased prefrontal cortex function is associated with poor CFF performance [40]. CFF has been tested to distinguish glaucoma from healthy patients [41], temporal modulation sensitivity in amblyopia [42], or as a biomarker in age-related macular degeneration [43].

Few studies have reported the physical factors affecting CFF [44,45,46,47,48]. Liu et al. [45] compared the CFF between normal vision and myopic students; they found a slight decrease in CFF value in those myopic students but without statistical significance. However, Chen et al. [49] found differently, showing reduced CFF values in patients with high.

In addition, in a recent study, we found a relationship between high-order aberrations and visual temporal resolution [36]. Therefore, optical factors degrading the retinal image quality seem to be influencing the visual CFF.

Together with ocular optical aberrations, scattering effects are the main factors degrading the retinal image quality [50]. One of the main pathological conditions inducing ocular scattering is cataracts [51]. Studies have shown that CFF is not affected by either early-stage or severe cataracts [52,53].

However, it should be considered that in the presence of cataracts, not only is the spatial resolution degraded, but the retinal brightness is also degraded due to scattered light. This loss of retinal brightness has led to adjusting the adequate flicker brightness for a better assessment of the CFF [54,55], but in those cases, if the Ferry–Porter law is applied [55], the real CFF value could be masked by an increase in the mean luminance of the flicker light leading to a compensation effect.

In our study, we employed a previously reported Arduino-based device to measure chromatic CFF in young adult subjects under straylight conditions. Those conditions were induced using Bangerter foils that have been reported to simulate forward scattering comparable to cataracts [37]. The technological performance of the Arduino was discussed in our previous study [36]. In short, Arduino microcontrollers provide performance equivalent to high-resolution DAQs at a much more affordable cost.

The experimental data demonstrated a statistically significant increase in CFF thresholds under straylight conditions (p < 0.05). This study underscores the impact of straylight on temporal visual processing, with findings that highlight its potential to enhance temporal resolution as measured by CFF thresholds. The observed increase aligns with theories suggesting an inhibitory interaction between parvocellular and magnocellular pathways [56], wherein reduced spatial resolution under straylight conditions may enhance temporal sensitivity. This adaptation could reflect an evolved neural mechanism aimed at maintaining visual functionality under suboptimal viewing conditions.

Van den Berg et al. [34] extensively reviewed the history and implications of ocular straylight, emphasizing its negative impact on spatial resolution, particularly in conditions of glare or low contrast. However, few studies have explored its temporal effects, making our findings a valuable contribution to this underexplored area. The observed enhancement of CFF under straylight aligns with the hypothesis that straylight disrupts parvocellular-driven spatial processing, thereby shifting reliance to magnocellular pathways, which are critical for temporal resolution.

Similarly, Mankowska et al. [57] reviewed temporal visual measures, noting that CFF can serve as a robust marker of magnocellular pathway function.

The compensatory mechanism behind parvocellular inhibition and magnocellular enhancement can be well understood by paying attention to the type and functionality of the ganglion cells that project information from the retina to the lateral geniculate nucleus [58].

Magnocellular cells (P-cells) respond with high sensitivity to stimuli resulting from a combination of high spatial frequency, low temporal frequencies, and high luminance contrast. On the contrary, in parvocellular cells (P-cells), the optimal response occurs to a combination of stimuli with high temporal frequency, low spatial frequencies, and low luminance contrast [59]. In that sense, the effects of a glare source or induced scattering effects on a resolution target would mainly affect the high spatial frequencies and luminance contrast (due to the energy distribution induced by light scattering), reducing the P-cells response sensitivity. If the visual target starts moving or flickering at a high temporal frequency, those factors that reduce the P-cell response will allow better M-cell performance.

Our findings offer implications for both clinical and practical scenarios. While optical corrections typically focus on minimizing straylight to improve spatial resolution [33], these results suggest the potential benefits of leveraging straylight to enhance temporal vision. This is particularly relevant for individuals working in environments with frequent flickering stimuli or for patients with visual impairments requiring optimized temporal sensitivity.

These findings are consistent with prior studies indicating that the visual system adapts to optical aberrations by altering neural processing patterns. For instance, enhanced flicker sensitivity has been documented under conditions of low illumination or optical scattering, with implications for understanding the neural plasticity of visual pathways [60].

Moreover, the present study uses EEG to examine the brain response to chromatic and achromatic flicker retinal stimulation at the critical flicker fusion frequency (CFF).

The role of electroencephalography (EEG) in detecting neural adaptations under straylight conditions is critical. Previous studies have demonstrated that VEPs are sensitive to visual impairments caused by optical scattering [61,62].

Our results extend this understanding by showing that straylight-induced changes may amplify this function, reflecting a neural compensation mechanism. In Section 3.1 and Section 3.2, it has been shown, on the one hand, that forward-induced scattering enhances the temporal resolution of vision by increasing the chromatic and achromatic CFF and, on the other hand, that flickering the visual field at the CFF with green and blue light can significantly increase alpha and beta brain activity and delta oscillations for green CFF stimulus only. Conversely, for an achromatic stimulus at the CFF, induced scattering reduces beta oscillations.

Deodato and Melcher [19] demonstrated that neural synchronization within the beta frequency band supports enhanced temporal processing under challenging visual conditions.

Our results not only confirm this sensitivity but also show that straylight enhances neural synchrony in the beta band, suggesting an active neural adjustment to maintain temporal sensitivity. This supports the findings by Biasiucci et al. [62], who highlighted the role of beta oscillations in integrating sensory information during challenging tasks.

We found that increased beta band activity suggests enhanced neural engagement in visual flicker processing. Beta oscillations in the occipital and parietal regions are known to be associated with sensory integration and attentional modulation [63]. Thus, scattering may act as a stimulus driving compensatory neural mechanisms to optimize temporal perception, which is described below:

If the contrast sensitivity is sufficiently reduced by degrading optical factors such as scattering, the attention to a visual stimulus may disappear completely, as described in the Troxler effect [64]. The prefrontal cortex plays an important role in the control of memory, concentration, and attention, among other cognitive functions [65]. Our findings revealed reduced activity at the pre-frontal lobe due to induced scattering. This reduced brain attention may be translated into a loss of spatial contrast sensitivity in visual perception, which is consistent with the reduced brain activity in all frequency bands in the occipital lobe when recording EEG signals after induced-scattering effects.

On the other hand, the cortical area that responds to visual motion perception is the temporal lobe, in which a significant increase in the brain’s electrical activity was found upon inducing scattering effects. Thus, the neural adaptations found to scattering are, on the one hand, a reduction in cognitive attention and, on the other hand, an increase in all frequency bands at the temporal lobe.

Puce et al. [66] suggested that motion information can be integrated into the temporal cortex even in low-contrast visual tasks using event-related potentials (ERPs). Our findings showed that the low-level visual stimulus induced by scattering results in reduced activity in the visual cortex but a significant increase in the temporal lobe, which then increases the temporal resolution power of the visual function.

Furthermore, the 8-channel configuration of our EEG device allowed us to explore the brain’s electrical activity at the occipital, temporal, frontal, and prefrontal lobes, revealing that the main implications of scattering in brain oscillations occur at frontal and temporal lobes.

5. Conclusions

This study demonstrates that induced forward-scattering significantly enhances temporal resolution by raising CFF thresholds and modulating neural activity in the brain. This adaptive response highlights the interplay between optical and neural factors in visual processing. Future research could explore the long-term effects of straylight or scattering on neural plasticity and its potential therapeutic applications for visual impairments.

The integration of psychophysical and electrophysiological methods, as demonstrated here, represents a powerful approach to advancing our understanding of dynamic visual perception.

This study provides novel evidence of the role of straylights in enhancing temporal resolution, as reflected by elevated CFF thresholds and associated neural activity. While previous research has focused primarily on the detrimental effects of straylight on spatial vision, our findings align with emerging evidence that straylight can influence temporal visual processing in a potentially beneficial manner.

In addition, our results contribute to a broader understanding of neural plasticity in vision. The ability of the brain to adapt to straylight by enhancing temporal processing aligns with the notion of cross-modal compensation, a concept supported by studies of visual and auditory integration [67].

While chromatic stimuli have demonstrated excitatory stimulation of high-frequency brain waves, achromatic retinal stimulation under scattering conditions is capable of inhibiting beta oscillations. Then, the combination of our previously reported Arduino-powered device with EEG measurements can reveal changes in the excitatory–inhibitory balance in the primary motor cortex [68], a constitutive biomarker of brain plasticity.

So, if the stimulus frequency is reduced to a perceptible flicker, and an induced scattering reduces the luminance contrast, P and M cells would be predominantly sensitive to color changes. That is, the Arduino-based visual stimulator configured in the subthreshold CFF will preferentially stimulate the parvocellular pathway with the chromatic stimuli and the magnocellular pathway with the achromatic stimulus.

To conclude, our study provides new insights into how ocular scattering influences cortical activity, extending beyond previous psychophysical approaches.

From a clinical perspective, these results suggest potential applications for improving visual performance in environments prone to glare or for individuals with conditions involving straylight. Additionally, the findings may guide the development of advanced optical correction systems that optimize both spatial and temporal visual aspects.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Health Sciences Institute of Aragon, Spain (protocol code: C.P.-C.I. PI20/377, date of approval: 14 July 2020).

Informed Consent Statement

Written informed consent has been obtained from the patient(s) to publish this paper.

Data Availability Statement

EEG raw data is available upon reasonable request to the corresponding author.

Conflicts of Interest

The author declares no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Electrode position configured for the study. ch#1: O1; ch#2: O2; ch#3: T5; ch#4: T6; ch#5: F7; ch#6: F8; ch#7: Fp1 and ch#8: Fp2. GND and REF (gray and green circles) correspond to ground signal and reference electrodes, respectively.

Figure 2. CFF values as a function of chromatic red (a), green (b), blue (c), and achromatic (d) stimuli for normal vision and after inducing ocular scattering. An asterisk indicates a statistical difference with p value lower than 0.05.

Figure 3. Raw EEG signals for natural viewing conditions (Control) and each electrode location for a total recording of 1 s acquired from one volunteer. The upper and lower dashed lines represent ± 2 mV amplitude levels.

Figure 4. EEGs filtered for delta, theta, alpha, beta, and gamma for a steady achromatic visual stimulus (control) and for flicker visual stimuli at the CFF for red, green, blue, and achromatic (white) colors. The recording time corresponds to 1 s. The amplitude signal is given in millivolts.

Figure 5. EEGs filtered for delta, theta, alpha, beta, and gamma for a steady achromatic visual stimulus affected by scattering (Scattering) and for scattering-induced flicker visual stimuli at the CFF for red, green, blue, and white (achromatic) colors. The recording time corresponds to 1 s. The amplitude signal is given in millivolts.

Figure 6. Average band power (ABP) for the 8-channel activity for control (blue bars), chromatic CFF flicker stimuli ((a–c) red, green, and blue bars), and achromatic ((d) gray bars) as a function of the filtered wave band. Asterisks indicate those wave bands for which significant differences were found between control and CFF flicker stimuli with significance level p [less than] 0.05.

Figure 7. Topographic brain activity maps filtered by wave band corresponding to the chromatic flicker stimuli (green and blue CFF), for which a statistical change in ABP with respect the control measurements were found. The scale color bar is in millivolts.

Figure 8. Average band power (ABP) for the 8-channel activity for control (a), chromatic CFF flicker stimuli ((c–e) red, green, and blue bars), and achromatic ((b) gray bars) as a function of the filtered wave band. Asterisks indicate those wave bands for which significant differences were found between control and CFF flicker.

Figure 9. Topographic maps of electrical brain activity as a function of the visual flicker condition. The scale bar is expressed in millivolts.

Figure 10. Energy density changes (%) for each electrode channel as a function of the visual stimuli. Blue bars correspond to scattering-free retinal flickering, and red bars the results under scattering effects.

Figure 11. Morlet Wavelet for control (a), pure scattering (c), blue CFF stimulus (b), and scattering-induced blue CFF stimulus (d) and transforms and their corresponding power spectral density (PSD) values [(e,f)] for channel O2.

Figure 12. Morlet Wavelet for control (a), pure scattering (c), blue CFF stimulus (b), and scattering-induced blue CFF stimulus (d) and transforms and their corresponding power spectral density (PSD) values [(e,f)] for channel T6.

Figure 13. Morlet Wavelet for control (a), pure scattering (c), blue CFF stimulus (b), and scattering-induced blue CFF stimulus (d) and transforms and their corresponding power spectral density (PSD) values [(e,f)] for channel F8.

Figure 14. Morlet Wavelet for control (a), pure scattering (c), blue CFF stimulus (b), and scattering-induced blue CFF stimulus (d) and transforms and their corresponding power spectral density (PSD) values [(e,f)] for channel Fp2.

Table 1

Description of the conditions of the visual stimuli on which the EEG signal was recorded.

Condition	Type	Description
#1	Control	Visual fixation of a steady White LED punctual source
#2	Scattering	Observation of control condition wearing a glasses frame with Bangerter foils
#3	Red CFF	Visual fixation of an LED flickering at the subject’s red CFF
#4	Green CFF	Visual fixation of an LED flickering at the subject’s green CFF
#5	Blue CFF	Visual fixation of an LED flickering at the subject’s blue CFF
#6	White CFF	Visual fixation of an LED flickering at the subject’s achromatic (White light) CFF
#7	Red CFF + Scattering	Visual fixation of an LED flickering at the subject’s red CFF + induced scattering
#8	Green CFF + Scattering	Visual fixation of an LED flickering at the subject’s green CFF + induced scattering
#9	Blue CFF + Scattering	Visual fixation of an LED flickering at the subject’s blue CFF + induced scattering
#10	White CFF + Scattering	Visual fixation of an LED flickering at the subject’s achromatic CFF + induced scattering

Table 2

Versatile 8ch EEG device technical specifications.

Wireless Amplifier	8-Channel Array
Sampling rate/resolution	256 Hz/24 bits
Bandwidth	DC-40 Hz
Input range and noise	±100 mV, <1µV RMS
Transmission	Bluetooth 2.1

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