ss-logo
Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages
Filter by Categories
Book Review
Brief Report
Case Letter
Case Report
Case Series
Commentary
Current Issue
Editorial
Erratum
Guest Editorial
Images
Images in Neurology
Images in Neuroscience
Images in Neurosciences
Letter to Editor
Letter to the Editor
Letters to Editor
Letters to the Editor
Media and News
None
Notice of Retraction
Obituary
Original Article
Point of View
Position Paper
Review Article
Short Communication
Short Communications
Systematic Review
Systematic Review Article
Technical Note
Techniques in Neurosurgery
ss-logo
Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages
Filter by Categories
Book Review
Brief Report
Case Letter
Case Report
Case Series
Commentary
Current Issue
Editorial
Erratum
Guest Editorial
Images
Images in Neurology
Images in Neuroscience
Images in Neurosciences
Letter to Editor
Letter to the Editor
Letters to Editor
Letters to the Editor
Media and News
None
Notice of Retraction
Obituary
Original Article
Point of View
Position Paper
Review Article
Short Communication
Short Communications
Systematic Review
Systematic Review Article
Technical Note
Techniques in Neurosurgery
ss-logo
Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages
Filter by Categories
Book Review
Brief Report
Case Letter
Case Report
Case Series
Commentary
Current Issue
Editorial
Erratum
Guest Editorial
Images
Images in Neurology
Images in Neuroscience
Images in Neurosciences
Letter to Editor
Letter to the Editor
Letters to Editor
Letters to the Editor
Media and News
None
Notice of Retraction
Obituary
Original Article
Point of View
Position Paper
Review Article
Short Communication
Short Communications
Systematic Review
Systematic Review Article
Technical Note
Techniques in Neurosurgery
View/Download PDF

Translate this page into:

Original Article
ARTICLE IN PRESS
doi:
10.25259/JNRP_351_2024

The influence of peripheral foot pressure on the brain’s alpha and theta waves while executing an emotional stimulus

, ,
1Department of Biomedical Engineering, National Institute of Technology, Raipur, Chhattisgarh, India.
2Department of Humanities and Social Sciences, National Institute of Technology, Raipur, Chhattisgarh, India.

*Corresponding author: Arindam Bit, Department of Biomedical Engineering, National Institute of Technology, Raipur, Chhattisgarh, India. arinbit.bme@nitrr.ac.in

Received: , Accepted: ,
© 2025 Published by Scientific Scholar on behalf of Journal of Neurosciences in Rural Practice
Licence
This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

How to cite this article: Mishra B, Tarai S, Bit A. The influence of peripheral foot pressure on the brain’s alpha and theta waves while executing an emotional stimulus. J Neurosci Rural Pract. doi: 10.25259/JNRP_351_2024

Abstract

Objectives:

Emotional task execution is a mid-level cognitive activity of the human brain. Higher cognitive load causes parasympathetic exertion. This hinders the formation of an exact execution pathway for the exposed emotional task. The human mind redistributes parasympathetic exertion by regulating peripheral pressure. This aids in allocating cognitive load in the brain.

Materials and Methods:

Here, the relationship between foot pressure and the cognitive burden of attention in the human brain was investigated while performing an emotional task under the effect of an attentional paradigm.

Results:

The results indicated gender differences in attention and emotion processing, suggesting that females have responded to local attentional stimuli along with emotional stimuli more quickly and accurately than males. Electroencephalography (EEG) results revealed that the early sensory components of N100 and P100 were modulated by global attention and happy emotion. Further, we found that global attention and happy emotion were marked by increased theta and alpha band oscillation networks.

Conclusion:

This study explores the neurocognitive mechanisms that improve the ability to manage conflicting tasks involving emotion–attention alongside cognitive load. The findings contribute to creating a novel approach to enhancing cognitive function associated with attentional stimuli.

Keywords

Attentions
Cognitive science
EEG
Emotions
Foot pressure

INTRODUCTION

Peripheral sensory input, including pressure on the feet, may influence brain activity. Research suggests that psychological pressure can increase cortico-spinal excitability and muscular activity in a lower limb reaction task.[1] Furthermore, emotional stimuli are known to modulate brainwave activity, with different emotions potentially eliciting distinct patterns in alpha and theta bands. Execution of emotional cues has a substantial association with the attentional paradigms.[2,3] Using a neural signature, emotion-attention interactions were discovered.[4] A multi-level analysis was developed to examine attention-emotion relationships and neural responses to reappraisal.[5,6] Smooth control of cognitive emotion greatly improves an individual’s well-being.[7] A substantive association exists between emotional intelligence and regulatory cognitive abilities during the execution of attention-demanding tasks.[8] In this regard, the present study intends to establish a neural cross-talk between emotion-attention and foot pressures to understand the relationship between the peripheral nervous system (PNS) and the central nervous system.

Emotional intelligence integrates various forms of emotion regulation (ER), including comprehension, management, and application, highlighting its strong link to cognitive processes.[9] The ventromedial prefrontal cortex, left dorsolateral prefrontal cortex, and anterior cingulate cortex modulate emotional responses based on attention and integrate affective information into decision-making.[10,11] In addition, the thalamus, amygdala, insula, and hippocampus process emotional significance and associate emotions with context.[12,13] It is fascinating how peripheral pressure dynamics influence emotional response modulation. Evaluating motor performance and the temporal influence model (TIMER) can predict ER. Adaptive or maladaptive ER influences sports performance. The TIMER can quantify this impact.[14] This paradigm suggests that ER imposes time-dependent demands on motor and cognitive resources, requiring coherence between physical and mental states for optimal outcomes.

Gender significantly influences emotion-driven cognitive decision-making. Studies examined gender-based audiovisual stimuli in artificial intelligence task performance[15] and found that gender-biased emotion selection enhanced systemic solutions.[16] In business, gender impacts consumer emotional behavior in retail decision-making.[17] Studies reveal gender differences in cognitive responses to emotional stimuli in alcoholism. Alcoholic men show lower activation in the rostral middle frontal cortex, parietal cortex, and precentral gyrus, while alcoholic women exhibit higher activation in the supramarginal cortex and superior frontal region.[18] These differences suggest gender-specific effects on cognitive function, with peripheral stimuli playing a minor role.

A strong correlation was found between affective states and emotional expressions, with positive affection linked to positive emotional identification.[19] Emotional responses were also more likely to trigger indirect perception. Reactive motor actions are strongly influenced by emotional cues, with stop-signal tasks (SSTs) commonly used to assess emotional intervention. In the absence of rapid emotional stimuli, SST reaction time reflects motor cortex inhibitory control.[20] The emotional component of motor control depends on stimulus valence, significance, and intensity, shaping neuro-cognitive models. Tactile sensory sensitivity also serves as a measure of emotion, as tactile exploration influences pupil dilation.[21] Strong emotional stimuli increase pupil diameter, while neutral stimuli keep it constricted, with greater dilation observed in women than men.[22] Initially, the autonomic nervous system reacts to extreme arousal, followed by pupil diameter regulation based on cognitive demands. In cognitive load modulation, peripheral sensors, such as flashing brake lights, influence response times (RTs) in driving. Greater brake light eccentricity increases cognitive load, leading to delayed responses.[23]

Research examined gender influences on hemispheric brain responses to emotional stimuli, evaluating double neurofeedback’s effect on functional symmetry.[24] Using a two-source lead-lag dichotic paradigm, findings revealed unique sound perception properties and stimulus-specific lateralization effects. Negative imagery and words stimulated mental processes, activating the extrastriate visual cortex in both hemispheres, with dorsal region expansion for images and superior temporal sulci activation for negative faces.[25,26] Negative phrases engaged the left inferior frontal and angular gyrus, key language-processing areas,[27] while the amygdala showed the strongest response to images.[28]

Word priming with gender influence modulates the lateralization effect, with emotive words processed faster in the left hemisphere for males and bilaterally for females.[29] The left hemisphere plays a key role in affective emotion stimulation. Different frequency bands redistribute cognitive loads, with theta and delta bands linked to post-emotional control behaviors,[30] enhancing cognitive regulation. Comparing emotional stimuli revealed stronger responses to audio than visual input, measured by the alpha-beta ratio in negative emotions. Delta-beta coupling supports attentional control, while mid-frontal theta activity modulates response inhibition to emotional stimuli.[31] Low-frequency changes occur 200 ms post-stimulus, with theta phase shifts linked to distancing from negative stimuli. Theta activity in posterior regions aids cognitive redistribution, serving as a regulatory catalyst. Theta frequency reflects motor responses in the mid and superior frontal regions, while video-induced emotional arousal attenuates alpha and beta band activity in cortical regions.[27,32] High arousal leads to alpha desynchronization, enhancing sensory processing in central and parietal regions. Studies confirm attenuated alpha and beta power in high-arousal states, with late alpha/beta increases indicating top-down inhibitory control.[2] Alpha power serves as an emotional processing tool, demonstrating proactive control over emotional distractions. Proactive control adjusts attentional configurations to reduce spontaneous distractions.[33] High-frequency post-stimulus conditions weaken suppression, but sustained proactive control minimizes distractions regardless of emotional influence. Neural oscillations reflect emotional valence processing across positive and negative stimuli. Further, a clinical study evaluated cognitive responses to non-verbal emotional stimuli in children with autism spectrum disorder. Fearful sounds had no effect, increasing alpha frequency peaks, while crying and laughter elicited typical EEG responses.[34] Finally, beta-band activity was analyzed for naturalistic emotional stimuli, revealing upper beta connectivity’s role in differentiating emotional valences.[35] Peripheral sensors were found to modulate alpha-band power, with the Minimum Spanning Tree (MST) method identifying informative emotional state frequencies.[36] Lower alpha and gamma bands significantly contributed to emotion discrimination. Musical sounds optimized stress responses, while emotional arousal triggered fundamental brain-body interactions. Valence classification of multimodal methods further assessed brain-body emotional responses.[37,38]

This study explores the strong relationship between attention-emotion processes, peripheral sensors, and their impact on central and autonomic nervous system functions. Despite extensive research, the influence of external pressure on cognitive resource redistribution and gender differences in processing remains unexplored. Addressing this gap, the study aims to enhance understanding of cognitive resource optimization for higher-order processes.

MATERIALS AND METHODS

Participants

The study involved a total of 15,200 trials with 8800 male trials and 7200 female trials. Participants were healthy, right-handed bilingual students (aged 22–28) with English as their mother tongue. In the behavioral experiment (n = 15200 trials, Female = 7200 trials), all participants achieved over 75% response accuracy (RA). An initial screening confirmed normal visual, neurological, and mental health. Written consent was obtained, and the study protocol was approved by the Institute’s Ethics Committee.

Stimulus and stimulus presentation

In the non-clinical study, emotional mechanisms in attention to expressive faces were assessed using a rapid odd-ball and mismatch paradigm. Happy and sad facial images, overlaid with global and local attention cues, served as stimuli. Participants identified the numbers 6 or 9 while focusing on attentional visuals, regardless of local or global information. Trials included randomized presentations of global happy (GH), local happy (LH), global sad (GS), and local sad (LS) stimuli, each shown for 500 ms and repeated 50 times. Stimuli were presented using SuperLab 5.0 on a 17-inch liquid-crystal display screen with black and grey images on a white background. Participants sat on a chair 60 cm away from the screen, in a dark, sound-attenuated chamber. Instructions were provided at the beginning, with participants pressing “F” for global (large) numbers and “G” for local (small) numbers. Each stimulus was followed by a black screen (infinite response window) until a key press was recorded. A synchronizer flagged stimulus categories for data marking, and SuperLab recorded RTs and accuracy for analysis. Trial timing followed standard emotional paradigm protocols, with the schematic shown in Figure 1.

Sample trial structure and stimulus timeline during the experiment.
Figure 1:
Sample trial structure and stimulus timeline during the experiment.

RT and accuracy analysis procedures

RA% and RT in ms were calculated for 200 trials per stimulus type. Data were extracted from the SuperLab sheet based on stimulus markers (GH, GS, LH, and LS). Participants were categorized by gender: Females (GR) and males (BY). RA was determined using true positive and true negative responses from the overall responses of true positive, false positive, true negative, and false negative. RT and RA were analyzed using analysis of variance (ANOVA) in the Statistical Package for the Social Sciences (IBM Inc., India) to assess correlations across groups and stimuli. A cutoff RA score of 75% was applied. Mean RT values (50 per stimulus category) were evaluated and visualized as bar diagrams. Descriptive statistics illustrated key dataset features, including interaction connectivity. Effect size was calculated from estimated parameters, and intra- and inter-subject effects were analyzed across time stamps. Statistical significance (p ≤ 0.05) was determined for global-local (G-L), happy-sad (H-S), boy-girl (BY-GR), and footwear conditions (with-footwear/without-footwear). Significant mean values were further examined for main and interaction effects.

EEG and foot pressure data acquisition

EEG and foot pressure data acquisition and processing steps were similar to those used in our earlier work.[35] A 16-channel bipolar data acquisition system (AD Instruments, Australia) was employed to record 10-channel EEG data through a 10– 20 electrode montage. The setup included a 10–20 EEG cap, bio-signal famplifier, PowerLab, and a Stim Tracker, which synchronized stimulus events from SuperLab (v5.0) with EEG recordings on LabChart (v8.1.5). Electrode impedance was kept below 5 Ω (verified using UFI Checktrode 989ES), with EEG signals band-pass filtered between 0.1 Hz and 40 Hz and samples at 2 kHz, meeting the Nyquist rate for 700 ms trial windows. For detailed description see Mishra et al., 2023.[35] In addition, six Force Sensing Resistor sensors (three per foot: toe-T, mid-M, heel-H) were embedded in a force plate platform (shoe sole size) (Malvade 2017;[39] Siddiqui et al., 2024).[40] Pressure signals were compared to a baseline using a comparator. Non-zero outputs were amplified with a precision amplifier and fed into six channels of the PowerLab system to capture left and right foot pressure signatures. Participants completed trials in two conditions – wearing thick-soled shoes (with-footwear: W) and barefoot (without-footwear: WO) – with foot pressure recorded across 200 trials. The Stim Tracker synchronized event markers across EEG and foot pressure recordings.

EEG signal processing and event-related potentials (ERP) analysis procedures

EEG signal processing involved segmentation into 700 ms time windows (100 ms pre-stimulus and 600 ms post-stimulus) for each stimulus type. Signals were further divided by frequency bands: delta (0.1–4 Hz), theta (4.1–8 Hz), alpha (8.1–13 Hz), and beta (13.1–40 Hz). Data from 50 trials per condition were averaged, and baseline artifacts (e.g., muscle activity, eye blinks) were minimized by squaring the signal to reduce noise magnitude, followed by low-pass filtering within the selected frequency band. Processed signals were derived from ten EEG channels (FP1, FP2, F3, F4, F7, F8, C3, C4, T3, T4), representing key brain regions including the prefrontal, frontal, temporal, and central-parietal lobes. Mean amplitudes (in µV) were calculated for four conditions per gender: GH, GS, LH, and LS. ERPs were analyzed for interactions among stimulus type, gender, and foot pressure—for instance, the GH Boy with Footwear condition (GHBYW). ERP components (N and P) were evaluated within specific time windows (100– 250 ms, 251–400 ms, and 401–600 ms). A MATLAB-based CFTool removed low-frequency ripples and fitted the ERP curves with a sum-of-sines function optimized for minimal residual error relative to standard deviation. Foot pressure data, recorded simultaneously, were also segmented based on interaction effects, and signals were averaged to compute means and standard deviations. Outlier peaks beyond the standard deviation range were filtered out. Pressure curves for both feet were then plotted at three points for each significant interaction condition.

Spectral feature analysis

Acquired EEG data, segmented by frequency bands, were further analyzed for conditions demonstrating significant interaction effects identified during ERP analysis. To assess the power spectra across different frequency bands and electrode sites, the Hilbert Transform (HT) was applied using the EEGLAB toolbox in MATLAB. This method enabled the extraction of instantaneous amplitude and phase information, crucial for analyzing event-related spectral dynamics. The HT was implemented to derive the analytic signal, facilitating frequency-specific power estimation without altering the signal’s magnitude, though inducing a phase shift of –π/2.[40,41] This can be further understood by following equation 1:

Hxt=π−1∫−∞∞xτt−τ

Here, the gain of HT spectral remained unchanged, while a phase shift can be observed by a factor of -π/2.

This transform helps to study event-related synchronization, desynchronization, and functional connectivity in EEG data.

Neural source estimation using standardized low-resolution brain electromagnetic tomography (sLORETA)

Local neural activity across frequency bands and ERP components under significant interaction conditions was estimated using a predefined head model in the brainstorm toolbox (MATLAB). Source localization was carried out using sLORETA[42] which served as a beam-scanning algorithm to reconstruct spatial activation across cortical regions based on EEG electrode placement. This method interpolated a 3D distribution of pseudo-electric potential differences, allowing for the visualization of neural activation patterns associated with cognitive and affective processes.

Statistical evaluation methods

To assess the significance of interactions among experimental variables – including EEG amplitude, reaction time, and reaction accuracy (RA) – repeated-measures ANOVA was applied across all conditions. Mean and standard deviation were computed to evaluate inter-trial and inter-subject variability. Statistical significance was determined using F-values, P-values, and multivariate tests, with an alpha threshold set at 0.05. To assess the effects and interactions among four independent variables on dependent measures (e.g., EEG amplitude, reaction time, and accuracy), a four-way repeated-measures ANOVA was conducted.[43,44]

RESULTS

Behavioral study

The behavioral analysis revealed a notable primary effect of stimulus type (global vs. local), with a trend toward significance (p = 0.056). A significant main effect of gender was observed, F(1,19) = 9.152, p = 0.014, η2p = 0.054, indicating that females demonstrated higher RA (M = 94.813, standard deviation [SD] = 0.598) than males [Figure 2a]. In addition, a significant main effect of footwear condition (with vs. without footwear) emerged (p = 0.056), with participants performing more accurately when barefoot. A significant interaction was also identified between stimulus type (G-L) and footwear condition, p = 0.020. Specifically, participants wearing shoes showed greater accuracy when processing local stimuli, whereas barefoot participants more accurately identified global stimuli. Furthermore, a two-way ANOVA revealed a significant interaction between gender and emotional condition, F(1,19) = 6.544, p = 0.031, η2p = 0.422. Females outperformed males in processing both positive and negative emotional stimuli [Figure 2b]. Regarding RT, a significant main effect of gender was detected, F(1,19) = 11.439, p = 0.008, η2p = 0.560. Males exhibited longer RTs (M = 876.457 ms, SD = 33.176) compared to females (M = 831.825 ms, SD = 25.787), indicating faster processing speed in females [Figure 2c]. No significant interaction effects were observed for RT. All statistical values are summarized in Tables 1 and 2.

Statistical representation of behavioral measures: (a) Primary effect of gender on response accuracy; (b) interaction effect between gender and emotional condition on response accuracy; and (c) response time (ms) illustrating the main effect of gender. RA: Response Accuracy, RT: Response Time
Figure 2:
Statistical representation of behavioral measures: (a) Primary effect of gender on response accuracy; (b) interaction effect between gender and emotional condition on response accuracy; and (c) response time (ms) illustrating the main effect of gender. RA: Response Accuracy, RT: Response Time
Table 1: Statistical indicators of response latency.
Response time
Interaction F-value p-value η2p
BYGR 11.439 0.008 0.560

BYGR(Boys-Girls): main effect of gender, F-value from ANOVA indicates the higher variability between the group,p is the statsical significance and F(1,19) = 7.183, p = 0.025, η2o( partial eta squared) is a measure of the effect size

Table 2: Statistical indicators of response correctness.
Response accuracy
Interaction F-value p-value η2p
GL 4.786 0.056 0.347
BYGR 9.152 0.014 0.054
WWO 4.793 0.056 0.347
GL*WWO 8.028 0.020 0.471
HS*BYGR 6.544 0.031 0.422

BYGR: Boys-girls, GL: Global-local, HS: Happy-sad, WWO: With-footwear/without-footwear, BYGR values: Indicate main effect of BYGR on response accuracy in figure 2a HS*BYGR values: Indicate interaction effect of BYGR & HS on response accuracy in figure 2b GL*WWO: * Indicates interaction effect between global-local and with/without footwear HS*BYGR: * Indicates interaction effect between happy-sad and boy-girl

Time domain study

101-250 ms ERP analysis

During the N100 time window (101–250 ms), a significant three-way interaction was observed in the left central region between foot pressure, gender, and attentional focus, F(1,19) = 6.199, p = 0.034, η2p = 0.408. Males exhibited greater amplitudes when attending to global stimuli, regardless of footwear condition [Figure 3a-c, left foot], and showed increased right foot pressure when barefoot [Figure 3d and e], suggesting enhanced left foot regulation during tasks requiring global, emotionally positive attention. Source localization revealed activation in the C3 region under these conditions [Figure 3f-i]. A significant interaction between gender and emotion was also found in the left prefrontal cortex at the N100 level, F(1,19) = 5.288, p = 0.047, η2p = 0.370. Females showed higher amplitudes in response to happy versus sad stimuli [Figure 4a] and exerted greater foot pressure under positive emotional conditions compared to males [Figure 4be]. Regardless of gender, early neural responses to happy stimuli were localized to the FP1 region [Figure 4f-i]. All statistical outcomes are summarized in Table 3.

N100 ERP analysis (101–250 ms) for GHBYW and GHBYWO conditions at 4.1–8 Hz: (a) Statistical comparison of N100 amplitudes at C3 between GHBYW and GHBYWO groups; (b and c) left foot pressure distribution for GHBYW and GHBYWO conditions, respectively; (d and e) right foot pressure distribution for GHBYW and GHBYWO conditions, respectively; (f and g) N100 ERP waveforms at C3 for GHBYW and GHBYWO groups; (h and i) C3 source localization through sLORETA for each condition (black square - Indicates activation region). ERP: Event-related potentials, GHBYW: Global happy boy with footwear, GHBYWO: Global happy boy without-footwear, sLORETA: Standardized low-resolution brain electromagnetic tomography.
Figure 3:
N100 ERP analysis (101–250 ms) for GHBYW and GHBYWO conditions at 4.1–8 Hz: (a) Statistical comparison of N100 amplitudes at C3 between GHBYW and GHBYWO groups; (b and c) left foot pressure distribution for GHBYW and GHBYWO conditions, respectively; (d and e) right foot pressure distribution for GHBYW and GHBYWO conditions, respectively; (f and g) N100 ERP waveforms at C3 for GHBYW and GHBYWO groups; (h and i) C3 source localization through sLORETA for each condition (black square - Indicates activation region). ERP: Event-related potentials, GHBYW: Global happy boy with footwear, GHBYWO: Global happy boy without-footwear, sLORETA: Standardized low-resolution brain electromagnetic tomography.
N100 ERP analysis (101–250 ms) at 8.1–13 Hz for GHBYW and GHGRW conditions at FP1: (a) Statistical comparison of N100 amplitudes at FP1 between GHBYW and GHGRW groups; (b and c) left foot pressure distribution for GHBYW and GHGRW conditions, respectively; (d and e) right foot pressure distribution for GHBYW and GHGRW conditions, respectively; (f and g) N100 ERP waveforms at FP1; and (h and i) FP1 source localization through sLORETA for GHBYW and GHGRW groups (black square - Indicates activation region). ERP: Event-related potentials, GHBYW: Global happy boy with footwear, GHGRW: Global happy girl with footwear, sLORETA: Standardized low-resolution brain electromagnetic tomography.
Figure 4:
N100 ERP analysis (101–250 ms) at 8.1–13 Hz for GHBYW and GHGRW conditions at FP1: (a) Statistical comparison of N100 amplitudes at FP1 between GHBYW and GHGRW groups; (b and c) left foot pressure distribution for GHBYW and GHGRW conditions, respectively; (d and e) right foot pressure distribution for GHBYW and GHGRW conditions, respectively; (f and g) N100 ERP waveforms at FP1; and (h and i) FP1 source localization through sLORETA for GHBYW and GHGRW groups (black square - Indicates activation region). ERP: Event-related potentials, GHBYW: Global happy boy with footwear, GHGRW: Global happy girl with footwear, sLORETA: Standardized low-resolution brain electromagnetic tomography.
Table 3: Statistical values of all interactions at various ERP and brain regions.
Time domain statistical values
ERP Brain region Interaction F-value p-value η2p
N100 C3 GL*BYGR*WWO 6.199 0.034 0.408
N100 FP1 BYGR*HS 5.288 0.047 0.370
P100 F3 BYGR*HS*WWO 5.548 0.043 0.381
P100 F4 BYGR*HS*WWO 5.251 0.048 0.368
P200 T4 BYGR*HS*WWO 7.183 0.025 0.444
N300 FP2 BYGR*HS*WWO 5.158 0.049 0.364
P300 T4 BYGR*WWO 4.573 0.061 0.337
P500 FP1 GL*BYGR*WWO 4.405 0.065 0.329
P500 FP2 GL*BYGR*WWO 4.570 0.061 0.337
P500 T4 GL*BYGR*WWO 7.164 0.025 0.443

ERP: Event-related potentials, BYGR: Boys-girls, GL: Global-local, HS: Happy-sad, WWO: With-footwear/without-footwear, GL*BYGR*WWO: * Indicates interaction in between

An early positive ERP component (P100) in the frontal region showed a significant direct influence of gender, and emotion. This effect was observed at F3 during theta band activity, F(1,19) = 5.548, p = 0.043, η2p = 0.381 [Figure 5a], and at F4 during alpha band activity, F(1,19) = 5.251, p = 0.048, η2p = 0.368 [Figure 5b and Table 3]. These results indicate that happy emotional stimuli elicited stronger P100 amplitudes in females compared to sad stimuli. Lower-frequency brain activity was associated with reduced foot pressure, whereas higher frequencies required greater foot engagement [Figure 5c-f].

P100 ERP analysis (101–250 ms) for GHGRW and GHGRWO conditions at 4.1–8 Hz (F3) and 8.1–13 Hz (F4): (a and b) Bar plots showing statistical comparisons at F3 and F4; (c and d) left foot pressure distributions for GHGRW F3 and GHGRWO F4 condition, respectively; (e and f) right foot pressure distributions for GHGRW F3 and GHGRWO F4 condition respectively; (g and h) P100 ERP waveforms at F3 and F4; and (i and j) sLORETA-based source localization at F3 and F4 for GHGRW and GHGRWO groups, respectively (black square - Indicates activation region). ERP: Event-related potentials, GHGRW: Global happy girl with footwear, GHGRWO: Global happy girl without footwear, sLORETA: Standardized low-resolution brain electromagnetic tomography.
Figure 5:
P100 ERP analysis (101–250 ms) for GHGRW and GHGRWO conditions at 4.1–8 Hz (F3) and 8.1–13 Hz (F4): (a and b) Bar plots showing statistical comparisons at F3 and F4; (c and d) left foot pressure distributions for GHGRW F3 and GHGRWO F4 condition, respectively; (e and f) right foot pressure distributions for GHGRW F3 and GHGRWO F4 condition respectively; (g and h) P100 ERP waveforms at F3 and F4; and (i and j) sLORETA-based source localization at F3 and F4 for GHGRW and GHGRWO groups, respectively (black square - Indicates activation region). ERP: Event-related potentials, GHGRW: Global happy girl with footwear, GHGRWO: Global happy girl without footwear, sLORETA: Standardized low-resolution brain electromagnetic tomography.

Under low foot pressure conditions, P100 amplitudes were greater at F3 than at F4. Source localization also revealed lateralized frontal activation around 100 ms linked to foot pressure [Figure 5g-j]. In addition, a late positive component (P200) within 250 ms emerged in the temporal region, showing significant effects of foot pressure and gender at T4, F(1,19) = 7.183, p = 0.025, η2p = 0.444 [Figure 6a]. Males exhibited stronger P200 responses while exerting lower foot pressure, particularly favoring the left foot [Figure 6b-e]. Overall, ERP amplitudes were higher in males than in females during GH stimulus processing [Figure 6f-i].

P200 ERP analysis (101–250 ms) at 4.1–8 Hz for GHBYW and GHGRWO conditions at T4: (a) Bar plot comparing P200 amplitudes at T4; (b and c) left foot pressure distribution by condition; (d and e) right foot pressure distribution; (f and g) P200 ERP waveforms at T4 for each group; and (h and i) T4 source localization through sLORETA for GHBYW and GHGRWO conditions (black square - Indicates activation region). ERP: Event-related potentials, GHBYW: Global happy boy with footwear, GHGRWO: Global happy girl without footwear, sLORETA: Standardized low-resolution brain electromagnetic tomography.
Figure 6:
P200 ERP analysis (101–250 ms) at 4.1–8 Hz for GHBYW and GHGRWO conditions at T4: (a) Bar plot comparing P200 amplitudes at T4; (b and c) left foot pressure distribution by condition; (d and e) right foot pressure distribution; (f and g) P200 ERP waveforms at T4 for each group; and (h and i) T4 source localization through sLORETA for GHBYW and GHGRWO conditions (black square - Indicates activation region). ERP: Event-related potentials, GHBYW: Global happy boy with footwear, GHGRWO: Global happy girl without footwear, sLORETA: Standardized low-resolution brain electromagnetic tomography.

251-400 ms ERP analysis

At the time interval of 251–400 ms, a significant interaction of attention, gender and emotion was found in the right prefrontal region, F(1,19) = 5.158, p = 0.049, η2p = 0.364, attributed to N300 activity [Figure 7a]. A near-significant interaction between foot pressure and gender also emerged in the right temporo-cortical area with P300 activity, F(1,19) = 4.573, p = 0.061, η2p = 0.337 [Figure 7b]. Higher foot pressure was associated with stronger responses to happy stimuli than to sad stimuli [Figure 7c-f]. Notably, females wearing footwear exhibited N300 activity at FP2 [Figure 7g], whereas those barefoot showed P300 activity at T4 [Figure 7h]. Source localization indicated dominant right-hemisphere activation in this condition [Figure 7i and j].

N300 and P300 ERP analysis (251–400 ms) at 8.1–13 Hz for GSGRW and GHGRWO conditions at FP2 and T4: (a and b) Bar plots showing statistical comparisons at FP2 and T4; (c and d) left foot pressure distributions; (e and f) right foot pressure distributions; (g and h) N300 and P300 ERP waveforms at FP2 and T4, respectively; and (i and j) source localization through sLORETA at FP2 and T4 for each condition (black square - Indicates activation region). ERP: Event-related potentials, GSGRW: Global sad girl with footwear, GHGRWO: Global happy girl without footwear, sLORETA: Standardized low-resolution brain electromagnetic tomography.
Figure 7:
N300 and P300 ERP analysis (251–400 ms) at 8.1–13 Hz for GSGRW and GHGRWO conditions at FP2 and T4: (a and b) Bar plots showing statistical comparisons at FP2 and T4; (c and d) left foot pressure distributions; (e and f) right foot pressure distributions; (g and h) N300 and P300 ERP waveforms at FP2 and T4, respectively; and (i and j) source localization through sLORETA at FP2 and T4 for each condition (black square - Indicates activation region). ERP: Event-related potentials, GSGRW: Global sad girl with footwear, GHGRWO: Global happy girl without footwear, sLORETA: Standardized low-resolution brain electromagnetic tomography.

401-600ms ERP analysis

A delayed P500 response was identified in the prefrontal regions, demonstrating the interaction effect of attention emotion and gender at FP1. Males exhibited stronger attentional responses to sad emotional stimuli at FP1, F(1,19) = 4.405, p= 0.065, η2p = 0.329, and at FP2, ”an interaction effect of emotion,gender and foot pressure was noticed”, with highest effect for sad boy without foot wear. F(1,19) = 4.570, p= 0.061, η2p = 0.337 [Figure 8a and b]. Global tasks elicited higher left foot pressure with reduced right foot exertion [Figure 8c-f]. The P500 amplitude was greater at FP2 than FP1 [Figure 8g-j]. In females, high-frequency (alpha band) P500 activity showed a significant interaction among foot pressure, attention, and gender. Temporal lobe analysis revealed a significant lateral effect at T4, F(1,19) = 7.164, p= 0.025, η2p = 0.443, and a trend-level effect at T3, F(1,19) = 3.903, p= 0.080, η2p = 0.303 [Figure 9a and b]. Global stimuli were linked to increased left foot pressure [Figure 9c-f], and source localization indicated higher activation at T3 than T4 during global tasks [Figure 9g-j and Table 3].

P500 ERP analysis (401–600 ms) at 4.1–8 Hz for GSBYW and LSBYWO conditions at FP1 and FP2: (a and b) Bar plots comparing P500 amplitudes at FP1 and FP2; (c and d) left foot pressure distributions by condition for GSBYW and LSBYWO conditions at FP1 and FP2, respectively; (e and f) right foot pressure distributions for GSBYW and LSBYWO conditions at FP1 and FP2, respectively; (g and h) P500 ERP waveforms at FP1 and FP2; and (i and j) sLORETA-based source localization at FP1 and FP2 for GSBYW and LSBYWO groups. (black square - Indicates activation region) ERP: Event-related potentials, GSBYW: Global sad boy with footwear, LSBYWO: Local sad boy without footwear, sLORETA: Standardized low-resolution brain electromagnetic tomography.
Figure 8:
P500 ERP analysis (401–600 ms) at 4.1–8 Hz for GSBYW and LSBYWO conditions at FP1 and FP2: (a and b) Bar plots comparing P500 amplitudes at FP1 and FP2; (c and d) left foot pressure distributions by condition for GSBYW and LSBYWO conditions at FP1 and FP2, respectively; (e and f) right foot pressure distributions for GSBYW and LSBYWO conditions at FP1 and FP2, respectively; (g and h) P500 ERP waveforms at FP1 and FP2; and (i and j) sLORETA-based source localization at FP1 and FP2 for GSBYW and LSBYWO groups. (black square - Indicates activation region) ERP: Event-related potentials, GSBYW: Global sad boy with footwear, LSBYWO: Local sad boy without footwear, sLORETA: Standardized low-resolution brain electromagnetic tomography.
P500 ERP analysis (401–600 ms) at 8.1–13 Hz for GHGRWO and LHGRWO conditions at T4 and T3: (a and b) Bar plots comparing P500 amplitudes at T4 and T3; (c and d) left foot pressure distributions T4 and T3; (e and f) right foot pressure distributions T4 and T3; (g and h) P500 ERP waveforms at T4 and T3; and (i and j) source localization through sLORETA at T4 and T3 for each condition (black square - Indicates activation region). ERP: Event-related potentials, GHBYW: Global happy girl without footwear, LHGRWO: Local happy girl without footwear, sLORETA: Standardized low-resolution brain electromagnetic tomography.
Figure 9:
P500 ERP analysis (401–600 ms) at 8.1–13 Hz for GHGRWO and LHGRWO conditions at T4 and T3: (a and b) Bar plots comparing P500 amplitudes at T4 and T3; (c and d) left foot pressure distributions T4 and T3; (e and f) right foot pressure distributions T4 and T3; (g and h) P500 ERP waveforms at T4 and T3; and (i and j) source localization through sLORETA at T4 and T3 for each condition (black square - Indicates activation region). ERP: Event-related potentials, GHBYW: Global happy girl without footwear, LHGRWO: Local happy girl without footwear, sLORETA: Standardized low-resolution brain electromagnetic tomography.

Frequency domain study

Theta band

Analysis across ten theta channels revealed a near-significant main effect of gender (p = 0.074) and an interaction between emotion and attention (p = 0.087), with males showing the highest amplitude [Figure 10a] and global attention aligning with happy emotions [Figure 10b]. Channel-wise statistical examination highlighted multiple effects in the right prefrontal region. A gender effect was most prominent in males, F(1,19) = 4.770, p = 0.057, η2p = 0.346 [Figure 10c]. A near-significant interaction between attention and gender (p = 0.089) showed elevated right prefrontal activation in males under global attention. In addition, a significant interaction between gender and emotion was observed, F(1,19) = 5.683, p = 0.041, η2p = 0.387, with sad emotional stimuli eliciting the strongest response in males. An almost significant interaction between emotion and foot pressure (p = 0.099) indicated higher amplitudes during sad emotions while wearing footwear. Another near-significant interaction (p = 0.080) between attention, gender, and foot pressure showed the greatest effect in males under global attention while wearing footwear at right pre-frontal. Time-frequency maps derived from HTs (Hilbert Transform) visualized cortical current dynamics in the right prefrontal cortex [Figure 10 d - l].

Statistical and time frequency analysis of electroencephalography amplitude and cortical currents for theta frequency: (a) Primary effect of gender; (b) interaction between emotion (happy/sad) and attention (global/local). Channel-specific statistical representation of amplitude bar graph: (c) Primary effect of gender; (d) Hilbert transform-based time-frequency map at FP2 for gender effect; (e) Gender × attention interaction; (f) corresponding time-frequency decomposition at FP2 for effect of gender and attention; (g) gender × emotion interaction; (h) corresponding time-frequency decomposition at FP2 for gender × emotion interaction; (i) emotion × foot pressure interaction; (j) corresponding time-frequency decomposition at FP2 for emotion × foot pressure interaction; (k) three-way interaction (gender × attention × foot pressure); (l) corresponding time-frequency decomposition at FP2 with interaction effect of attention, gender, and foot pressure; (m) primary effect of emotion; (n) Hilbert-based cortical current map at F3 for emotion effect; (o) interaction between attention, gender, and foot pressure; (p) corresponding time-frequency decomposition at F3 for interaction effect of attention, gender, and foot pressure; (q) interaction of attention, gender, and emotion; (r) time-frequency decomposition corresponding to F4 with effect of attention, gender and emotion; (s) gender × foot pressure interaction; and (t) corresponding time-frequency decomposition at C4 with effect of gender and foot pressure.
Figure 10:
Statistical and time frequency analysis of electroencephalography amplitude and cortical currents for theta frequency: (a) Primary effect of gender; (b) interaction between emotion (happy/sad) and attention (global/local). Channel-specific statistical representation of amplitude bar graph: (c) Primary effect of gender; (d) Hilbert transform-based time-frequency map at FP2 for gender effect; (e) Gender × attention interaction; (f) corresponding time-frequency decomposition at FP2 for effect of gender and attention; (g) gender × emotion interaction; (h) corresponding time-frequency decomposition at FP2 for gender × emotion interaction; (i) emotion × foot pressure interaction; (j) corresponding time-frequency decomposition at FP2 for emotion × foot pressure interaction; (k) three-way interaction (gender × attention × foot pressure); (l) corresponding time-frequency decomposition at FP2 with interaction effect of attention, gender, and foot pressure; (m) primary effect of emotion; (n) Hilbert-based cortical current map at F3 for emotion effect; (o) interaction between attention, gender, and foot pressure; (p) corresponding time-frequency decomposition at F3 for interaction effect of attention, gender, and foot pressure; (q) interaction of attention, gender, and emotion; (r) time-frequency decomposition corresponding to F4 with effect of attention, gender and emotion; (s) gender × foot pressure interaction; and (t) corresponding time-frequency decomposition at C4 with effect of gender and foot pressure.

In the left frontal region, a marginal main effect of emotion (p = 0.082) and an interaction of attention, gender, and foot pressure (p = 0.090) were observed. Positive emotions induced the highest activity, with males under global attention and barefoot conditions showing the strongest response. Time-frequency mapping confirmed the association between happy emotions and increased left foot pressure [Figure 10 m - p].

A significant three-way interaction among attention, gender, and emotion was found in the right frontal region, F(1,19) = 11.244, p = 0.008, η2p = 0.555. Females demonstrated heightened activity under local attention with sad emotion [Figure 10q], further visualized through Hilbert-transformed data [Figure 10r]. A near-significant gender-by-foot pressure interaction (p = 0.062) in the right central region showed greater influence in barefoot females [Figure 10s], with supporting cortical current maps [Figure 10t]. Overall, these findings suggest distinct frontal and central theta activity patterns, with males predominantly influencing right prefrontal regions and females impacting right central and frontal regions.

Alpha band

In the alpha band, a marginally significant interaction between gender and foot pressure was found in the left prefrontal region (p = 0.073), with females exerting higher foot pressure showing the greatest impact, as revealed by Hilbert-transformed EEG and cortical current mapping [Figure 11a and b]. A near-significant main effect of foot pressure (p = 0.096) and an interaction between gender and emotion (p = 0.062) were observed in the left frontal region, where males with increased foot pressure in response to sadness showed the strongest effects [Figure 11c-f].

Statistical and spectral characterization of event-related potentials amplitudes for alpha frequency: (a) interaction effect of gender and foot pressure at FP1; (b) time-frequency map of electroencephalography and cortical currents at FP1; (c) primary effect of foot pressure; (d) time-frequency map at F3 showing foot pressure effects; (e) interaction of gender and emotion; (f) time-frequency map at F3 for gender– emotion interaction; (g) primary effect of gender; (h) time-frequency map at F7 showing gender effects; (i) interaction of emotion, gender, and foot pressure; (j) time-frequency map at F7 for the interaction of emotion, gender and foot pressure; (k) gender–emotion interaction at C3; (l) corresponding time-frequency map at C3; (m) gender–emotion interaction at C4; (n) time-frequency map at C4; (o) interaction of attention, gender and foot pressure; and (p) corresponding time-frequency map at T4.
Figure 11:
Statistical and spectral characterization of event-related potentials amplitudes for alpha frequency: (a) interaction effect of gender and foot pressure at FP1; (b) time-frequency map of electroencephalography and cortical currents at FP1; (c) primary effect of foot pressure; (d) time-frequency map at F3 showing foot pressure effects; (e) interaction of gender and emotion; (f) time-frequency map at F3 for gender– emotion interaction; (g) primary effect of gender; (h) time-frequency map at F7 showing gender effects; (i) interaction of emotion, gender, and foot pressure; (j) time-frequency map at F7 for the interaction of emotion, gender and foot pressure; (k) gender–emotion interaction at C3; (l) corresponding time-frequency map at C3; (m) gender–emotion interaction at C4; (n) time-frequency map at C4; (o) interaction of attention, gender and foot pressure; and (p) corresponding time-frequency map at T4.

In the left mid-frontal region, both a near-significant main effect of gender (p= 0.065) and a three-way interaction of gender, emotion, and foot pressure (p= 0.065) were detected. The most pronounced influence was seen in males experiencing sadness under low foot pressure [Figure 11g-j]. A significant interaction between gender and emotion emerged in the left central region (F(1,19) = 5.799, p = 0.039, η2p = 0.392), with the strongest response among males under the effect of negative emotion [Figure 11k].

In the right central region, a near-significant gender–emotion interaction (p = 0.077) was observed, primarily affecting females experiencing sad emotion. Gender-related lateralization in the central region was further supported by time-frequency mapping [Figure 11l-n]. Finally, a near-significant interaction among attention, gender, and foot pressure (p = 0.088) was found in the right temporal area, showing the strongest effects in females under local attention and low foot pressure [Figure 11o and p].

DISCUSSION

Behavioral results indicated that local attentional stimuli elicited higher RA compared to global stimuli. This supports the notion that local attention engages more top-down and executive processes, which demand greater cognitive resources.[45] However, this contrasts with prior findings suggesting global attention typically yields better accuracy and faster response times.[46] The present discrepancy may stem from the interaction between attention type, emotional valence (happy vs. sad), and foot pressure. Cultural background, psychopathology, and expertise likely modulate this preference for local processing[47] which has been associated with more rigid and compulsive traits. Gender differences were also evident, with females outperforming males in processing both attentional and emotional stimuli. Prior studies confirm that women are more accurate and faster in responding to local stimuli[48] and exhibit superior emotional recognition skills.[49] These findings align with evidence suggesting women are generally more attentive to emotional cues and more efficient in processing emotionally salient information.[50] In addition, happy emotional stimuli were processed more rapidly than sad or unpleasant ones across genders, supporting previous findings that positive emotions broaden attentional scope, whereas negative emotions narrow it.[51]

Early N100 and P100 are associated with global attention and happy emotion processing

ERP findings revealed that males exhibited higher N100 amplitudes (101–250 ms) when processing global versus local attentional stimuli, consistent with prior studies linking early ERP components (P100 and N150) to attention-related stimulus evaluation.[26,52] Specifically, males showed greater N150 responses to global cues.[53] Particularly under high foot pressure without footwear, indicating increased cognitive effort in central brain regions.[54] Emotion also modulated N100 amplitudes, with happy stimuli eliciting greater central activity in females and stronger prefrontal activation across genders – supporting the idea that global attention and positive emotion are associated with fast, automatic processing.[55] Although females responded more strongly to happy emotions, they appeared to engage fewer cognitive resources for global attentional cues compared to males. The P100 component further highlighted gender differences, with females showing higher amplitudes in response to happy faces, suggesting enhanced early emotional processing. In addition, foot pressure modulated P100, with greater frontal activation observed under high pressure without footwear. Overall, these results support the notion that happy emotions broaden attentional scope (reflected in N100) for both genders, while P100 is particularly sensitive to early emotional recognition in females.

P200 component modulates happy emotion for male participants

P200 amplitudes (150–250 ms) were significantly higher in males during the processing of global-happy emotional stimuli under low foot pressure with footwear, specifically in the temporal region. This suggests that males require greater cognitive resources in this context, highlighting a gender-specific effect in early emotional-attentional processing. The P200 component is known to reflect heightened sensitivity to emotionally and cognitively salient stimuli[2] and is typically enhanced for positive emotional faces.[25] Gender-specific differences in P200 have been previously reported, particularly with males exhibiting stronger responses to global visual stimuli in frontal and temporal regions.[56] This supports the view that men possess a distinctive advantage in processing global attentional scope,[57] contradicting earlier claims of no gender differences in G-L attentional bias.[58]

P300 and N300 amplitudes differentiate female and male processing of emotion

A pronounced N300 negative deflection was observed in the right prefrontal region (FP2) when females processed happy emotional stimuli with low foot pressure (with footwear), suggesting that foot pressure modulates emotional processing in this phase. Conversely, high P300 amplitudes were detected in the right temporal region (T4) during the processing of happy stimuli under high foot pressure without footwear, indicating increased cognitive-emotional engagement. Interestingly, both N300 and P300 effects exhibited right-hemispheric dominance, challenging previous findings that rejected hemispheric asymmetry in gendered emotional processing.[59] Supporting past research, females showed greater P300 amplitudes than males,[5,60] aligning with the emotional value hypothesis which posits that P300 reflects the affective evaluation of stimuli.[61] These findings reinforce the notion that females exhibit stronger late-stage emotional ERP responses, particularly in P300 and N400 components.[59]

Global attention with higher foot pressures modulates P600 components

When processing global attentional stimuli with high foot pressure (barefoot), males exhibited increased P600/Late Positive Complex (LPC) amplitudes in both left (FP1) and right (FP2) prefrontal regions. In addition, both genders showed enhanced P600 amplitudes in the temporal regions when processing global stimuli while wearing footwear. These findings align with prior research indicating that the LPC is sensitive to emotionally and attentively salient stimuli, eliciting greater amplitudes for both linguistic and non-linguistic visual stimuli.[62] In contrast to neutral stimuli, emotionally charged phrases or images were linked to more positive LPC.[5] Our results suggest that frontal and prefrontal LPC activity supports the processing of global attention, particularly in emotionally relevant contexts.

Global attention and happy emotion marked by increased theta and alpha band oscillations

Theta band activity in the right prefrontal region showed gender-specific differences, with males exhibiting elevated theta frequencies during the processing of global attention and happy emotion, particularly under low foot pressure. This suggests enhanced right prefrontal engagement in males for global attention and emotional processing. In contrast, females demonstrated increased alpha activity in the left prefrontal cortex during high foot pressure conditions, indicating greater alpha demands when barefoot. Males also showed elevated alpha power in the left frontal region for happy emotion and in the left central region for sad emotion with high foot pressure, whereas females exhibited greater alpha activity in the right central region for sad emotional stimuli. Both theta and alpha oscillations are closely linked to selective attention and emotional processing in prefrontal and frontal regions.[63] Moreover, males showed increased theta activity in the right prefrontal region when processing sad faces, while females displayed heightened theta in the right frontal lobe during sad emotion under local attention. These findings support previous research indicating that frontal theta oscillations are sensitive to emotionally salient and attentionally significant stimuli.[64-66]

CONCLUSION

The research findings revealed gender-based differences in attentional processing, with females demonstrating faster and more accurate responses to local stimuli and superior emotional recognition. EEG findings showed that early sensory components (N100 and P100) were modulated by global attention and positive emotion, suggesting an expanded attentional span across early and later ERP stages. Enhanced theta and alpha oscillations were also associated with global attention and positive emotion. Foot pressure variations, particularly in females, influenced attentional task performance, whereas males showed symmetrical foot pressure distribution. These results offer novel insights into the role of sensorimotor factors – like foot pressure – in modulating cognitive load and enhancing attentional engagement. The findings hold implications for clinical, educational, and developmental contexts, particularly in understanding the interplay between central and PNSs. Moreover, the association between attention types and emotional valence offers a potential avenue for Attention-Deficit Hyperactivity Disorder (ADHD) research. A key limitation of this study is the small sample size, future research should address to validate and expand on these findings.

Acknowledgement:

Authors acknowledge the funding resources and the participants for assisting in conducting the study. Furthermore, authors acknowledge the support of undergraduate students Vinod Ratre for assisting the conduction of experiment.

Authors’ Contributions:

Bit A and Tarai S conceptualized and designed the research study. Mishra B conducted the research and performed the data analysis. Bit A provided guidance and support in the development of the manuscript. Mishra B and Bit A collaboratively drafted the manuscript. All authors contributed to the editorial revisions and approved the final version of the manuscript.

Ethical approval:

The study was approved by the Institutional Ethics Committee of NIT Raipur (Approval No. NITRR/IEC/2022/35), dated May 13, 2022.

Declaration of patient consent:

The authors certify that they have obtained all appropriate patient consent.

Conflicts of interest:

There are no conflicts of interest.

Use of artificial intelligence (AI)-assisted technology for manuscript preparation:

The authors confirm that no artificial intelligence (AI)-assisted technology was used to assist in the writing or editing of the manuscript, and no images were manipulated using AI.

Financial support and sponsorship: The study had been supported by TEQIP program of NIT Raipur for providing a scholarship to the research fellow B. Mishra. The study is also supported by the seed funding of NIT Raipur to S. Tarai.

References

  1. , . Increased corticospinal excitability and muscular activity in a lower limb reaction task under psychological pressure. J Funct Morphol Kinesiol. 2017;2:14.
    [CrossRef] [Google Scholar]
  2. , . Emotion and brain oscillations: High arousal is associated with decreases in alpha-and lower beta-band power. Cereb Cortex. 2021;31:1597-608.
    [CrossRef] [PubMed] [Google Scholar]
  3. , , , , , . Alpha and theta oscillations during the cognitive reappraisal of aversive pictures: A spatio-temporal qEEG investigation. Int J Psychophysiol. 2023;192:13-25.
    [CrossRef] [PubMed] [Google Scholar]
  4. , , , , , , et al. Neural correlates of emotion-attention interactions: From perception, learning, and memory to social cognition, individual differences, and training interventions. Neurosci Biobehav Rev. 2020;108:559-601.
    [CrossRef] [PubMed] [Google Scholar]
  5. , , . Event-related potentials, emotion, and emotion regulation: An integrative review. Dev Neuropsychol. 2010;35:129-55.
    [CrossRef] [PubMed] [Google Scholar]
  6. , , , , . A cognitive control framework for understanding emotion regulation flexibility. Emotion. 2020;20:21-9.
    [CrossRef] [PubMed] [Google Scholar]
  7. . Emotion regulation, attention to emotion, and the ventral attentional network. Front Hum Neurosci. 2013;7:746.
    [CrossRef] [PubMed] [Google Scholar]
  8. , . Cognitive control and emotional intelligence: Effect of the emotional content of the task. Brief reports. Front Psychol. 2019;10:195.
    [CrossRef] [PubMed] [Google Scholar]
  9. , , , , . Bi-Directional LSTM Classification of Emotions in EEG Signals In: 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). India: IEEE; . p. :1385-90.
    [CrossRef] [Google Scholar]
  10. , , . The ventromedial prefrontal cortex and emotion regulation: Lost in translation? J Physiol. 2023;601:37-50.
    [CrossRef] [PubMed] [Google Scholar]
  11. , , , , , , et al. Anodal tDCS effects over the left dorsolateral prefrontal cortex (L-DLPFC) on the rating of facial expression: Evidence for a gender-specific effect. Heliyon. 2021;7:e08267.
    [CrossRef] [PubMed] [Google Scholar]
  12. , . A short review on emotion processing: A lateralized network of neuronal networks. Brain Struct Funct. 2022;227:673-84.
    [CrossRef] [PubMed] [Google Scholar]
  13. , , , , , , et al. Understanding emotions: Origins and roles of the amygdala. Biomolecules. 2021;11:823.
    [CrossRef] [PubMed] [Google Scholar]
  14. , . Emotion regulation and motor performance: An integrated review and proposal of the temporal influence model of emotion regulation (TIMER) Int Rev Sport Exerc Psychol. 2020;13:266-96.
    [CrossRef] [Google Scholar]
  15. , , , , . Emotion elicitation under audiovisual stimuli reception: Should artificial intelligence consider the gender perspective? Int J Environ Res Public Health. 2020;17:8534.
    [CrossRef] [PubMed] [Google Scholar]
  16. , , . Examining and mitigating gender bias in text emotion detection task. Neurocomputing. 2022;493:422-34.
    [CrossRef] [Google Scholar]
  17. , , , . Consumer decision-making in a retail store: The role of mental imagery and gender difference. Int J Retail Distrib Manag. 2020;49:421-45.
    [CrossRef] [Google Scholar]
  18. , , , , , , et al. Alcoholism gender differences in brain responsivity to emotional stimuli. eLife. 2019;8:e41723.
    [CrossRef] [PubMed] [Google Scholar]
  19. , , . Affective states influence emotion perception: Evidence for emotional egocentricity. Psychol Res. 2021;85:1005-15.
    [CrossRef] [PubMed] [Google Scholar]
  20. , , , , . Frozen in (e)motion: How reactive motor inhibition is influenced by the emotional content of stimuli in healthy and psychiatric populations. Behav Res Ther. 2021;146:103963.
    [CrossRef] [PubMed] [Google Scholar]
  21. , , , , , . Emotion measurements through the touch of materials surfaces. Front Hum Neurosci. 2020;13:455.
    [CrossRef] [PubMed] [Google Scholar]
  22. , . Enlargement of female pupils when perceiving something cute. Sci Rep. 2021;11:23367.
    [CrossRef] [PubMed] [Google Scholar]
  23. , , , , . Detection of brake lights while distracted: Separating peripheral vision from cognitive load. Atten Percept Psychophys. 2019;81:2798-813.
    [CrossRef] [PubMed] [Google Scholar]
  24. , , , . Does double biofeedback affect functional hemispheric asymmetry and activity? A pilot study. Symmetry. 2021;13:937.
    [CrossRef] [Google Scholar]
  25. , , , , , . Fast unconscious processing of emotional stimuli in early stages of the visual cortex. Cereb Cortex. 2022;32:4331-44.
    [CrossRef] [PubMed] [Google Scholar]
  26. , , , , . Negative content enhances stimulus-specific cerebral activity during free viewing of pictures, faces, and words. Hum Brain Mapp. 2020;41:4332-54.
    [CrossRef] [PubMed] [Google Scholar]
  27. , , , , , , , et al. The angular gyrus as a hub for modulation of language-related cortex by distinct prefrontal executive control regions. J Cogn Neurosci. 2022;34:2275-96.
    [CrossRef] [PubMed] [Google Scholar]
  28. . Multidimensional processing in the amygdala. Nat Rev Neurosci. 2020;21:565-75.
    [CrossRef] [PubMed] [Google Scholar]
  29. , , , , , . Lateralized affective word priming and gender effect. Front Psychol. 2019;9:2591.
    [CrossRef] [PubMed] [Google Scholar]
  30. , , , . Built to last: Theta and delta changes in resting-state EEG activity after regulating emotions. Brain Behav. 2022;12:e2597.
    [CrossRef] [PubMed] [Google Scholar]
  31. , , . Mid-frontal theta modulates response inhibition and decision making processes in emotional contexts. Brain Sci. 2019;9:271.
    [CrossRef] [PubMed] [Google Scholar]
  32. , , . Emotion classification using EEG brain signals and the broad learning system. IEEE Trans Syst Man Cybern Syst. 2021;51:7382-91.
    [CrossRef] [Google Scholar]
  33. , , , . Proactive control of emotional distraction: Evidence from EEG alpha suppression. Front Hum Neurosci. 2020;14:318.
    [CrossRef] [PubMed] [Google Scholar]
  34. , . Atypical EEG responses to nonverbal emotionally charged stimuli in children with ASD. Behav Neurol. 2020;2020:2807946.
    [CrossRef] [PubMed] [Google Scholar]
  35. , , , . Processing of attentional and emotional stimuli depends on retrospective response of foot pressure: Conceptualizing neuron-cognitive distribution in human brain. Comput Biol Med. 2023;164:107186.
    [CrossRef] [PubMed] [Google Scholar]
  36. , . EEG based emotion recognition using minimum spanning tree. Phys Eng Sci Med. 2020;43:985-96.
    [CrossRef] [PubMed] [Google Scholar]
  37. . Subjective effects of broadband water sounds with inaudible high-frequency components. Sci Rep. 2024;14:7627.
    [CrossRef] [PubMed] [Google Scholar]
  38. , , . Brain and body emotional responses: Multimodal approximation for valence classification. Sensors. 2020;20:313.
    [CrossRef] [PubMed] [Google Scholar]
  39. , , . IoT based monitoring of foot pressure using FSR sensor In: 2017 International Conference on Communication and Signal Processing (ICCSP); 2017 Apr 6-8; Chennai, India. Piscataway (NJ): IEEE; . p. :635-9.
    [CrossRef] [Google Scholar]
  40. , , , , , , et al. Footwear-integrated force sensing resistor sensors: A machine learning approach for categorizing lower limb disorders. Eng Appl Artif Intell. 2024;127:107205.
    [CrossRef] [Google Scholar]
  41. . Estimating and interpreting the instantaneous frequency of a signal. II. Algorithms and applications. Proc IEEE. 1992;80:540-68.
    [CrossRef] [Google Scholar]
  42. , , . An extended Hilbert transform method for reconstructing the phase from an oscillatory signal. Sci Rep. 2023;13:3535.
    [CrossRef] [PubMed] [Google Scholar]
  43. . Standardized low resolution brain electromagnetic tomography (sLORETA): Technical details. Methods Find Exp Clin Pharmacol. 2002;24(Suppl D):5-12.
    [Google Scholar]
  44. , . Design and analysis: a researcher's handbook (4th ed). Upper Saddle River (NJ): Pearson Prentice Hall; .
    [Google Scholar]
  45. . Discovering statistics using IBM SPSS statistics (5th ed). London: SAGE Publications; .
    [Google Scholar]
  46. . Does attentional selectivity in global/local processing improve discretely or gradually? Front Psychol. 2014;5:61.
    [CrossRef] [Google Scholar]
  47. , . Global precedence and response activation: Evidence from LRPs. Q J Exp Psychol Sect A. 2002;55:289-310.
    [CrossRef] [PubMed] [Google Scholar]
  48. , , , , , , et al. ERP evidence of a meaningfulness impact on visual global/local processing: When meaning captures attention. Neuropsychologia. 2011;49:1258-66.
    [CrossRef] [PubMed] [Google Scholar]
  49. , , , , , , et al. Gender differences in visual reflexive attention shifting: Evidence from an ERP study. Brain Res. 2011;1401:59-65.
    [CrossRef] [PubMed] [Google Scholar]
  50. , . Gender Differences in judgments of multiple emotions from facial expressions. Emotion. 2004;4:201-6.
    [CrossRef] [PubMed] [Google Scholar]
  51. , , , , , , et al. Are females more responsive to emotional stimuli? A neurophysiological study across arousal and valence dimensions. Brain Topogr. 2010;23:27-40.
    [CrossRef] [PubMed] [Google Scholar]
  52. , . Positive facial expressions are recognized faster than negative facial expressions, but why? Psychol Res Psychol Forsch. 2004;69:22-9.
    [CrossRef] [PubMed] [Google Scholar]
  53. , . Event-related potentials elicited by stimulus spatial discrepancy in humans. Neurosci Lett. 2002;326:73-6.
    [CrossRef] [PubMed] [Google Scholar]
  54. , , , , . Integrative structural, functional, and transcriptomic analyses of sex-biased brain organization in humans. Proc Natl Acad Sci U S A. 2020;117:18788-98.
    [CrossRef] [PubMed] [Google Scholar]
  55. , , . The wandering mind of men: ERP evidence for gender differences in attention bias towards attractive opposite sex faces. Soc Cogn Affect Neurosci. 2011;6:477-85.
    [CrossRef] [PubMed] [Google Scholar]
  56. , , . Tune in to the right frequency: Theta changes when distancing from emotions elicited by unpleasant images and words. Eur J Neurosci. 2021;53:916-28.
    [CrossRef] [PubMed] [Google Scholar]
  57. , , , . Neurofunctional organization of working memory on the basic characteristics of visual space in males and females. ?????????? ????????. 2023;49:28-41.
    [CrossRef] [Google Scholar]
  58. , . Global-local visual biases correspond with visual-spatial orientation. J Clin Exp Neuropsychol. 2004;26:24-30.
    [CrossRef] [PubMed] [Google Scholar]
  59. , , . Gender differences in global-local perception? Evidence from orientation and shape judgments. Acta Psychol (Amst). 2009;130:64-71.
    [CrossRef] [PubMed] [Google Scholar]
  60. , , , , , . Gender-selective effects of the P300 and N400 components of the visual evoked potential. Vision Res. 2008;48:917-25.
    [CrossRef] [PubMed] [Google Scholar]
  61. , , , . Electrophysiological and behavioral evidence of gender differences in the modulation of distraction by the emotional context. Biol Psychol. 2008;79:307-16.
    [CrossRef] [PubMed] [Google Scholar]
  62. , , , , . Effects of facial affect recognition on the auditory P300 in healthy subjects. Neurosci Res. 2001;41:89-95.
    [CrossRef] [PubMed] [Google Scholar]
  63. , . Event-related potential indices of masked repetition priming. Psychophysiology. 2003;40:115-30.
    [CrossRef] [PubMed] [Google Scholar]
  64. , . Human anterior and frontal midline theta and lower alpha reflect emotionally positive state and internalized attention: High-resolution EEG investigation of meditation. Neurosci Lett. 2001;310:57-60.
    [CrossRef] [PubMed] [Google Scholar]
  65. , , . Human brain EEG indices of emotions: Delineating responses to affective vocalizations by measuring frontal theta event-related synchronization. Neurosci Biobehav Rev. 2011;35:1959-70.
    [CrossRef] [PubMed] [Google Scholar]
  66. , . Frontal theta activity in humans increases with memory load in a working memory task. Eur J Neurosci. 2002;15:1395-9.
    [CrossRef] [PubMed] [Google Scholar]

Fulltext Views
1,924

PDF downloads
5,438
View/Download PDF
Download Citations
BibTeX
RIS
Show Sections

Suggested read for related articles: