Abstract

Objective:

This study investigated the effects of repeated transcranial direct current stimulation on response inhibition and sought to elucidate the underlying neurobehavioral mechanisms.

Methods:

In a randomized, triple-blind, sham-controlled, 36 male soccer players were assigned to active-tDCS, sham-tDCS, or no-intervention control groups. The active-tDCS group received 20-min 1.5 mA tDCS over the right dorsolateral prefrontal cortex (DLPFC) five times weekly for 4 weeks, alongside regular training. The sham-tDCS group received 1-min 1.5 mA tDCS with regular training, and the no-intervention control group only regular trained. Pre- and post-intervention, all participants performed a Go/No-go task while behavioral and event-related potential (ERP) data were recorded. Behavioral metrics: Go reaction time (RT), Go accuracy (ACC), and No-go accuracy. ERP metrics: P3 amplitude and latency.

Results:

Behavioral: Only the active-tDCS group showed significantly shorter Go RT post-intervention compared to baseline and the control group. The ACC for the three groups of Go and No-go tasks remained unchanged. ERP: Only the active-tDCS group exhibited increased P3 amplitude and reduced P3 latency during both Go and No-go trials. A significant three-way interaction indicated that latency shortening in No-go trials was most pronounced at central sites Cz/Cpz. The sham-tDCS group and the no-intervention control group showed no significant changes in P3 amplitude and latency between pre-tests and post-tests.

Discussion:

These preliminary findings suggest that repeated tDCS over the right prefrontal cortex may enhance behavioral response speed in soccer players, accompanied by neurophysiological changes indicative of optimized processing efficiency (increased P3 amplitude and shortened latency). However, given the exploratory nature and modest sample size, these results warrant confirmation in larger-scale studies.

Clinical trial registration:

https://www.chictr.org.cn/showproj.html?proj=288285, ChiCTR2500109387.

1 Introduction

Soccer is an open-skill sport where response inhibition, as a core component of executive function, is crucial (Ali, 2011; Wang et al., 2013). Response inhibition refers to the suppression of behaviors that are no longer needed or are inappropriate in rapidly changing environments (Verbruggen and Logan, 2008). It is crucial for an individual’s motor development, enabling rapid responses to constantly changing movements (Albaladejo-Garcia et al., 2023). For soccer players, possessing exceptional response inhibition enables them to execute tactical maneuvers with precision and immediately rescind the erroneous decision, thereby reducing errors and enhancing athletic performance (Beavan et al., 2019; Albuquerque et al., 2019). Consequently, response inhibition can be used to discriminate between skill levels in soccer players (Verburgh et al., 2014). Research indicates that higher-level soccer players typically exhibit superior executive function, with their response inhibition abilities positively correlated to their performance level in soccer (Vestberg et al., 2012). Traditionally, athletic training has been the primary means of enhancing response inhibition performance (Kao et al., 2022). However, given that cognitive functions are rooted in neural activity within the brain (Blasi et al., 2006), and neural networks involved in response inhibition exhibit significant plasticity (Chambers et al., 2009). This suggests that, in addition to athletic training, neuroscience technologies that regulate brain activity (Roelfsema et al., 2018) may also be effective approaches for enhancing response inhibition.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that modulates cortical excitability via a weak direct current (Fuster, 2008; Nitsche et al., 2008). It was first applied in the field of clinical medicine (Brunoni et al., 2012). Since pioneering research by German scholar Nitsche (Nitsche and Paulus., 2001) in 2001, tDCS has been demonstrated to effectively enhance motor performance (Hummel et al., 2005) and cognitive function (Borducchi et al., 2016). Notably, the underlying mechanism of tDCS involves polarity-specific modulation of cortical excitability. For instance, Carlsen et al. (2015) demonstrated that anodal tDCS typically increases excitability and shortens reaction times in simple tasks, whereas cathodal stimulation decreases excitability and slows responses. The above evidence indicates that tDCS has a positive effect on enhancing participants’ motor performance and cognitive function. However, existing research has primarily focused on investigating the acute effects of a single tDCS session (Stagg et al., 2018). There remains a lack of in-depth exploration into the cumulative benefits of repeated tDCS interventions in athletic populations and the underlying neural mechanisms.

To explore these neural mechanisms, non-invasive tools that directly measure brain activity are essential. Electroencephalography (EEG) (Cohen, 2017), which records electrical activity from the scalp, provides a direct window into the brain’s millisecond-level dynamics with high temporal resolution. The event-related potential (ERP) technique (Luck, 2014), derived from the time-locked averaging of EEG signals, is particularly suited for isolating brain activity associated with specific cognitive processes, such as response inhibition. Over the years, ERP has become one of the most widely used tools for assessing cognitive functions (Sur et al., 2009; Helfrich and Knight, 2019). Distinct ERP components serve as vital windows into studying cognitive functions (Donchin, 2022), providing an objective method for investigating brain activity. Classic ERP components include P1, N1, P2, N2, and P3 (Picton et al., 1995). Among the ERP components elicited by response inhibition tasks, N2 and P3 are regarded as two core indicators (Luijten et al., 2011; Rietdijk et al., 2014; Groom and Cragg, 2015). The N2 component (Patel and Azzam, 2005), a negative wave typically peaking around 200–300 ms post-stimulus, is thought to primarily reflect early conflict monitoring (Rietdijk et al., 2014). The P3 component (Patel and Azzam, 2005), a positive wave usually occurring between 300 and 500 ms after stimulus onset, is one of the most stable and extensively studied endogenous components (Gajewski and Falkenstein, 2013; Groom and Cragg, 2015; Hirao et al., 2020). Evidence suggests that P3 is more closely associated with the successful execution of inhibitory behavior, the late allocation of cognitive resources, and the final evaluation of decision-making (Rietdijk et al., 2014). While both N2 and P3 are implicated in inhibitory function, this study primarily focuses on analyzing the P3 component. The rationale is threefold. First, in sports contexts that demand rapid decision making and action execution, such as football, the later implementation stage of inhibitory control (P3) is postulated to have a more direct contribution to behavioral outcomes compared to the earlier conflict monitoring stage (N2) (Groom and Cragg, 2015). Second, P3 has been established as a robust and reliable electrophysiological marker of response inhibition, potentially offering greater consistency than N2 across studies and paradigms (Kropotov et al., 2011; Randall and Smith, 2011). Finally, research shows that long-term sport-specific training can enhance cognitive function and neural efficiency (Bonetti et al., 2025), with the P3 component serving as a sensitive measure of these adaptive neurocognitive changes (Wang et al., 2020; Prema et al., 2021). At its core, the Go/No-go paradigm operationalizes response inhibition by creating a conflict between the high-frequency Go responses and the occasional requirement to withhold a response on No-go trials (Gomez et al., 2007). Therefore, employing the Go/No-go paradigm to assess these mechanisms is well justified (Wright et al., 2014). Specifically, the P3 component is robustly elicited during successful No-go trials, serving as a direct neurophysiological index of inhibitory resource allocation. In this context, the amplitude of P3 reflects the intensity of cognitive engagement in the inhibitory process, while its latency marks the timing of this process relative to the stimulus (Picton, 1992).

In summary, while prior research has established the acute effects of single-session tDCS, the cumulative neurobehavioral impact of repeated tDCS in athletes remains underexplored, particularly using direct neural measures. To address this gap, this study employs a longitudinal, triple-blind, sham-controlled design to investigate whether a 4-week repeated tDCS protocol can induce sustainable enhancements in cognitive-motor performance in soccer players. In this exploratory study, we aim to investigate both the behavioral outcomes and the underlying neurophysiological mechanisms—specifically through modulation of the P3 component—that may mediate these potential improvements. Given the preliminary nature of repeated tDCS research in athletic populations, the present findings are intended to generate hypotheses and provide a foundation for future confirmatory trials. This approach allows us to move beyond verifying cumulative behavioral effects and toward explaining how repeated tDCS may optimize the neural efficiency of networks supporting response control in athletic populations. The findings are thus expected to provide a critical neurophysiological evidence base for advancing targeted, neuroscience-guided cognitive training protocols in sports science.

2 Materials and methods2.1 Experimental subject2.1.1 Sample size estimation and justification

A priori sample size estimation was performed using G*Power software (version 3.1) (Faul et al., 2007). Based on previous literature (Brysbaert, 2019; Voss et al., 2010) and the present study design, a repeated-measures analysis of variance (ANOVA) model was selected. The significance level (α) was set at 0.05, and the desired statistical power (1 − β) was set at 0.80. Given that previous studies have reported medium effect sizes for differences in inhibitory control among athletes (Willcutt et al., 2005), a medium effect size (Cohen’s f = 0.3) was used for the calculation. This analysis indicated that a minimum total sample size of 30 participants was required. To account for potential sample attrition and enhance the robustness of results, the final sample size was increased to 36 participants. Although the sample size was determined based on a priori power analysis, the findings should be interpreted as preliminary due to the relatively modest sample and the exploratory nature of this repeated tDCS protocol in athletes.

2.1.2 Participant recruitment and criteria

A total of 36 healthy male soccer players, all holding Chinese National Grade II Athlete certification or higher, were recruited for this study. All participants were right-handed to control for potential confounding effects of handedness on brain lateralization and tDCS-induced neuromodulation. They also had normal or corrected-to-normal vision. Individuals were excluded if they had a history of major surgery, neurological or psychiatric disorders, use of medications known to affect cognitive function, or prior participation in similar experiments within the past 6 months. Only male participants were enrolled to control for potential gender related confounding effects on the outcomes. Participants were recruited through convenience sampling from local university sports teams.

2.1.3 Ethics approval and trial registration

This study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol received approval from the Ethics Committee of the School of Physical Education, Jiangxi Normal University (Approval No. IRB-JXNU-PEC-2025013). Written informed consent was obtained from all participants prior to their involvement in the study. As a pre-specified sub-study of the prospectively registered trial “The Effect of Different Training Methods on the Response Inhibition Ability of Athletes with Open Skills/Closed Skills: A Study Based on ERP” (Chinese Clinical Trial Registry, Registration No. ChiCTR2500109387), this investigation was specifically designed to validate the neurophysiological effects of tDCS under rigorously controlled conditions to minimize placebo effects.

2.2 Experimental grouping and design

This study employed a triple-blind, randomized, sham-controlled design. Using SPSS software (version 26.0), an independent researcher not involved in subsequent procedures generated the randomization sequence and assigned the 36 participants to one of three groups (n = 12 per group): an active-tDCS group, a sham-tDCS group, and a no-intervention control group. One-way ANOVA confirmed that the groups did not differ significantly in terms of age, height, body weight, or years of training experience (all p > 0.05), indicating well-balanced baseline characteristics (Table 1).

GroupAge (years)Height (cm)Weight (kg)Training experience (years)Active-tDCS (n = 12)21.28 ± 2.25175.50 ± 4.5270.33 ± 7.135.91 ± 2.02Sham-tDCS (n = 12)21.36 ± 1.91174.75 ± 3.9369.58 ± 5.556.25 ± 1.71No-intervention control (n = 12)21.01 ± 1.52175.41 ± 3.8771.91 ± 3.986.08 ± 1.67F0.1080.1190.5230.102p0.8980.8880.5980.904

One-way ANOVA of baseline characteristics across the three groups.

Data are presented as mean ± standard deviation (M ± SD).

Following randomization, all participants completed a pre-test using the Go/No-go response inhibition task. This was followed by a 4-week intervention period, during which the active-tDCS group received verum stimulation alongside their regular training, the sham-tDCS group received sham stimulation alongside regular training, and the no-intervention control group engaged in regular training only. A post-test was administered upon completion of the intervention. Within the 24 h preceding each testing session, participants were instructed to refrain from strenuous physical exercise and the consumption of caffeine or alcohol. A flowchart of the experimental procedure is presented in Figure 1.

Flowchart of the experimental procedure. (A) Thirty-six soccer players were randomly assigned to three groups: active-tDCS, sham-tDCS, or no-intervention control (n=12 each). (B) All participants completed a Go/No-go task as a pre-test. (C) The active-tDCS group received 1.5 mA tDCS for 20 min per session, the sham-tDCS group received 1.5 mA for only 1 min, and the control group received no stimulation; all groups underwent regular training 5 sessions/week for 4 weeks. (D) A post-test Go/No-go task was administered to all participants. (E) Behavioral and ERP data were processed using MATLAB and statistically analyzed with SPSS 26.0.

2.3 tDCS intervention protocol

The tDCS intervention was administered using a DC-Stimulator PLUS device in an offline paradigm (i.e., stimulation was applied prior to cognitive testing). The stimulation parameters were selected based on established safety guidelines (Bikson et al., 2009; Bikson et al., 2016), previous studies (Borducchi et al., 2016), and the stimulation target was determined according to the research by Robertson and Marino (2016). The anode electrode (5 × 7 cm, 35 cm2) was positioned over the rDLPFC, corresponding to the F4 site according to the international 10–20 EEG system, with the cathode placed on the contralateral shoulder.

To computationally model and visualize the electric field distribution induced by this electrode montage, a simulation was performed using SimNIBS 4.1.0 (Saturnino et al., 2019). The simulation was based on the standard head model “Ernie.” As the model’s anatomical scope does not include the shoulder, the extracephalic cathode was modeled as a large electrode at the lower posterior aspect of the head—a standard simplification employed in prior research to approximate the current return pathway (Masina et al., 2021). This computational approach, grounded in anatomically precise head modeling (Datta et al., 2009), provides a reliable estimate of the cortical electric field under the anode, as supported by empirical validation studies (Huang et al., 2017). The results of this simulation, detailing the electrode configuration, cortical field distribution, and axial cross-section, are presented in Figure 2.

Electrode montage based on the 10–20 system and computational modeling of the induced electric field. (A) Schematic of the experimental electrode setup. The anode (red, 5 × 7 cm) was positioned over the right dorsolateral prefrontal cortex (rDLPFC). The cathode (blue, 5 × 7 cm) was placed on the contralateral shoulder. (B) Predicted electric field magnitude distribution on the cortical surface resulting from the montage shown in panel (A), simulated using SimNIBS. The field is predominantly localized to the prefrontal region under the anode. The color bar indicates the field strength. (C) International 10–20 system for EEG electrode placement, providing the standard reference for locating the F4 site (anode position) used in this study.

In the active-tDCS group, stimulation was delivered at an intensity of 1.5 mA for a duration of 20 min, incorporating 30-s fade-in and fade-out periods at the beginning and end of the session. For the sham-tDCS group, all parameters were identical to the active-tDCS group, except the stimulation duration was reduced to 1 min (excluding fade-in/fade-out periods) to mimic the authentic sensory experience and ensure the integrity of the blinding procedure. To ensure the efficacy of the tDCS intervention while avoiding interference with the athletes’ regular training schedule, the tDCS sessions were conducted on training days. Furthermore, to minimize potential confounds from circadian rhythms and physical fatigue, all tDCS sessions were administered approximately 1 h before the participants’ regular training sessions. The intervention lasted for 4 weeks, with 5 sessions administered per week.

2.4 Blinding procedures

This study employed a triple-blind, randomized, sham-controlled design, ensuring that participants, experimenters, and the statistician remained unaware of group allocation throughout the trial.

2.4.1 Allocation concealment

An independent researcher (randomization coordinator) who was not involved in participant recruitment, data collection, or analysis generated the randomization sequence using SPSS (version 26.0). This researcher prepared sequentially numbered, opaque, sealed envelopes. Each envelope contained the group assignment (active-tDCS, sham-tDCS, or no-intervention control) and the corresponding parameter instructions for the stimulation device.

2.4.2 Handling of the no-intervention control group

To maintain blinding in the control group, all participants were informed prior to enrollment that the study involved three different “recovery or enhancement protocols”: electrical stimulation, sensory control stimulation, or focused rest. Participants in the no-intervention control group followed an identical visit schedule as the tDCS groups (5 sessions per week, approximately 30 min per session). During each visit, they were seated in the same laboratory room, wore a head cap (to mimic electrode placement), and were instructed to rest quietly. The same experimenter who stayed with the tDCS participants also stayed with the control participants, providing the same level of interaction (e.g., checking comfort, ensuring stillness) to equate the amount of attention received. Thus, control participants were not aware that they were in a “no-intervention” condition.

2.4.3 Stimulation delivery procedure for tDCS groups

The specific intervention procedure for the active-tDCS and sham-tDCS groups was as follows: The randomization coordinator prepared sealed envelopes containing the parameter instructions. An experimenter (blind to group assignment) guided the participant into the laboratory and performed electrode placement according to the protocol described in Section 2.3. The experimenter then temporarily exited the room. An independent operator (not involved in any other aspect of the study) entered with the envelope, opened it, and configured the DC-Stimulator PLUS device in strict accordance with the enclosed instructions. After obscuring key information on the device screen and initiating the stimulation, the operator promptly departed. The experimenter re-entered the laboratory, remained with the participant throughout the 20-min intervention period, and completed standardized records. At the 20-min mark, the experimenter uniformly terminated the stimulation, concluding the session. For the sham-tDCS group, the device was programmed to deliver current only during the first minute (including 30-s fade-in and fade-out), while the experimenter and participant remained blind to this condition.

2.4.4 Blinding of data analysis

The statistician received a fully de-identified dataset, with groups coded as A, B, and C, and remained blind to the coding scheme until the primary analyses were completed.

2.4.5 Blinding assessment and adverse effects

Immediately after the post-test, all participants (including those in the no-intervention control group) were asked to guess which group they had been assigned to, with the following options: “active tDCS,” “sham tDCS,” or “unsure.” This allowed us to evaluate the success of participant blinding. Additionally, participants in the active-tDCS and sham-tDCS groups were asked to report any adverse sensations experienced during or after the stimulation sessions (e.g., tingling, itching, burning, headache, or discomfort). The frequency and severity of these symptoms were recorded for safety monitoring.

2.5 Response inhibition task and procedure

A classic Go/No-go paradigm was employed to assess participants’ response inhibition skill. The task was programmed using E-Prime software (version 3.0). As depicted in Figure 3, the visual stimuli consisted of soccer scenario images: Go Stimulus: The image depicted a cartoon character dribbling the ball forward, representing a legal and positive action in soccer. Participants were instructed to press the “spacebar” as quickly as possible in response. No-go Stimulus: The image depicted a cartoon character committing a clear intentional handball foul, representing a prohibited action in soccer. Participants were required to inhibit their key press response and refrain from any action. Prior to the formal experiment, a practice block of 10 trials was administered, during which performance feedback was provided to ensure task comprehension. In the main experiment, each trial began with a fixation cross “+” presented at the center of the screen for 500 ms, followed by the random presentation of either a Go or No-go stimulus for 1,000 ms. A blank screen was then displayed for an inter-stimulus interval of 200 ms. The entire task comprised a total of 200 trials, distributed across two blocks (100 trials per block). The stimulus probability was set at 70% for Go trials (n = 140) and 30% for No-go trials (n = 60). A short rest period was allowed between the two blocks. The total duration of the experiment was approximately 8–10 min. Prior to the experiment, all participants confirmed that they had no prior experience with the computerized Go/No-go task used in this study.

Go/No-go task flowchart.

2.6 Data processing and statistical analysis2.6.1 Behavioral data processing

Stimulus presentation and the collection of behavioral data were managed using E-Prime software (version 3.0). Data from practice trials and trials with an overall accuracy rate below 60% were excluded from the final analysis. Three primary behavioral measures were selected for analysis: Go reaction time (RT), Go trial accuracy (ACC), and No-go trial accuracy (ACC).

2.6.2 ERP data processing

Electroencephalogram (EEG) signals were recorded using a 64-channel ERP acquisition system from Neuroscan Company. Electrodes were positioned according to the international 10–20 system. The signal resolution was set at 100 nV with a sampling rate of 1,000 Hz per channel. The FCz and AFz electrodes served as the reference and ground, respectively, and all electrode impedances were maintained below 10 kΩ. Offline data processing was performed using the EEGLAB toolbox running in a MATLAB environment. The EEG data processing pipeline included the following steps: (1) Removal of irrelevant electrodes (M2, HEOG, VEOG); (2) Re-referencing to the average of all channels; (3) Band-pass filtering between 0.1 Hz and 30 Hz; (4) Epoching from −200 ms to 1,100 ms relative to stimulus onset, followed by baseline correction using the −200 ms to 0 ms pre-stimulus interval; (5) Manual rejection of epochs containing amplitudes exceeding ±100 μV; (6) Independent component analysis to identify and remove artifacts associated with eye blinks, eye movements, and cardiac activity; (7) Averaging of artifact-free epochs to derive the ERP waveforms for each condition.

Although both the N2 and P3 components are associated with response inhibition, the present study focused its analysis on the P3 component for the following theoretical and methodological reasons. First, the primary aim was to investigate the cumulative, neuroplastic effects of a 4-week repeated tDCS protocol. The P3 component, reflecting later-stage cognitive evaluation and resource allocation, has been demonstrated to be more sensitive to sustained training and neuromodulation effects on processing efficiency compared to the earlier, conflict-monitoring related N2 component (Wang et al., 2020). Second, the observed behavioral effect of interest—reduction in Go RT—is more directly linked to the functional interpretation of P3 (stimulus evaluation and response decision speed) than to N2 (initial conflict detection). Finally, concentrating statistical power on a single, robust component (P3) provided a more precise test of our primary hypothesis within the current sample size. Therefore, the P3 mean amplitude and peak latency were selected as the primary neural metrics for this study.

Based on visual inspection of the grand-averaged ERP waveforms across all participants and in line with previous literature (Picton, 1992; Vázquez-Marrufo et al., 2013), the P3 component was quantified within a 300–450 ms post-stimulus time window. For statistical analysis, we extracted both the mean amplitude and the peak latency of the P3 component from the three midline electrode sites (Cz, CPz, Pz). This dual-metric approach is well-established in ERP research, as it allows for the concurrent assessment of the overall magnitude of neural engagement (mean amplitude) and the precise timing of the peak positive deflection (peak latency), thereby capturing complementary aspects of cognitive processing (Ruggeri et al., 2019).

To ensure reliable peak latency detection against high-frequency artifacts, we employed a rigorous two-step procedure combining automated and visual verification. First, the local positive maximum within the 300–450 ms window was identified automatically for each electrode. Subsequently, all detected peaks underwent independent visual inspection by two trained researchers. For a peak to be accepted, it had to satisfy three key criteria: it needed to correspond to a clear, monophasic positive deflection in the individual ERP waveform; its morphology had to be consistent with the P3 component observed in the grand-average data; and it must be clearly distinguishable from residual artifacts or high-frequency oscillations. Discrepancies between raters were resolved through consensus discussion. This combined approach significantly enhances the robustness of latency estimation and minimizes the risk of Type I errors from noise-driven spurious peaks.

2.6.3 Statistical analysis

All statistical analyses were performed on behavioral and ERP measures. The normality of distribution for all dependent variables was first assessed using the Shapiro–Wilk test; all variables met the assumption of normality (p > 0.05), justifying the use of parametric repeated-measures analysis of variance (ANOVA). For behavioral measures, a two-way repeated-measures ANOVA was conducted with Group (active-tDCS, sham-tDCS, no-intervention control) and Time (pre-test, post-test) as factors on the following dependent variables: Go reaction time (RT), Go trial accuracy (ACC), and No-go trial ACC. For ERP measures, a three-way repeated-measures ANOVA was conducted with Group, Time, and Electrode (Cz, CPz, Pz) as factors on the P3 mean amplitude and peak latency. Separate analyses were performed for the Go and No-go conditions. Mauchly’s test was used to assess sphericity; when this assumption was violated, the Greenhouse–Geisser correction was applied. For significant main effects involving more than two levels, post hoc pairwise comparisons were performed using the Bonferroni correction. For significant interaction effects, simple effect analyses were conducted, followed by Bonferroni-corrected pairwise comparisons where appropriate. Partial eta-squared () is reported as the measure of effect size. According to Cohen’s convention, values of 0.01, 0.06, and 0.14 were interpreted as small, medium, and large effects, respectively (Cohen, 2013). The significance level (α) was set at 0.05 for all tests. Data are presented as mean ± standard deviation (M ± SD). All analyses were conducted using SPSS (version 26.0).

3 Research results3.1 Behavioral results

The results of the repeated-measures ANOVAs for all behavioral measures are summarized in Table 2, with statistically significant effects highlighted. For Go RT, a significant main effect of Time was observed, F(1,33) = 5.408, p = 0.026,  = 0.141, indicating that the mean RT was significantly shorter during the post-test (415.87 ± 6.24 ms) compared to the pre-test (433.84 ± 6.05 ms). Furthermore, a significant Time × Group interaction was found for Go RT, F(2,33) = 3.483, p = 0.042,  = 0.174. Simple effect analyses revealed that the active-tDCS group exhibited a significant reduction in RT from pre-test to post-test (p = 0.002, mean difference = 45.39 ms, 95% CI [18.16, 72.64]). In contrast, no significant changes were observed in the sham-tDCS group (p = 0.376) or the no-intervention control group (p = 0.796). Post-hoc pairwise comparisons with Bonferroni correction revealed no significant differences among the three groups at pre-test (all p > 0.05). At post-test, the active-tDCS group responded significantly faster than the no-intervention control group (p = 0.021, mean difference = 43.82 ms, 95% CI [5.37, 82.29]). However, the difference between the active-tDCS and sham-tDCS groups did not reach statistical significance (p = 0.148), nor did the difference between the sham-tDCS and no-intervention control groups (p = 0.785). No other main effects or interactions reached statistical significance.

MeasureEffectdfFpGo RTGroup2,331.4630.2460.081Time1,335.4080.026*0.141Time × Group2,333.4830.042*0.174Go ACCGroup2,330.2160.8070.013Time1,330.2000.6580.006Time × Group2,331.4000.2610.078No-go ACCGroup2,330.8550.4350.049Time1,330.0910.7640.003Time × Group2,330.4860.6190.029

Results of the repeated-measures ANOVA for behavioral measures.

Bold values indicate significant main effects or interaction effects (*p < 0.05).

3.2 ERP results

ERP data from all 36 participants (active-tDCS group: n = 12; sham-tDCS group: n = 12; no-intervention control group: n = 12) were included in the analysis. The differences in P3 amplitude and latency during Go and No-go tasks before and after the intervention are reported below.

3.2.1 P3 amplitude during go trials

To maximize statistical power for detecting the core effects of the tDCS intervention, we first averaged the P3 amplitude across the three electrode sites (Cz, CPz, Pz) and performed a two-way repeated-measures ANOVA with factors Group (active-tDCS, sham-tDCS, no-intervention control) and Time (pre-test, post-test). This analysis revealed a significant main effect of Time, F(1,33) = 23.721, p < 0.001,  = 0.418, indicating that P3 amplitudes were overall larger during the post-test (3.16 ± 0.25 μV) compared to the pre-test (2.52 ± 0.27 μV).

More importantly, a critical Time × Group interaction was found, F(2,33) = 21.255, p < 0.001,  = 0.563. Simple effect analyses (with Bonferroni correction) showed that only the active-tDCS group exhibited a significant increase in P3 amplitude from pre-test to post-test (p < 0.001, mean difference = 1.85 μV, 95% CI [1.388, 2.314]). In contrast, no significant changes were observed in the sham-tDCS group (p = 0.921) or the no-intervention control group (p = 0.842). Between-group comparisons indicated no significant differences in P3 amplitude at pre-test (all p > 0.05), confirming equivalent baseline levels. At post-test, the active-tDCS group demonstrated significantly larger P3 amplitudes than both the sham-tDCS group (p = 0.027, mean difference = 1.73 μV, 95% CI [0.162, 3.313]) and the no-intervention control group (p = 0.024, mean difference = 1.76 μV, 95% CI [0.190, 3.341]).

This interaction pattern is clearly visualized in Figure 4. The topographic maps (Figure 4A) show that at baseline, all three groups exhibited highly consistent spatial patterns of electrophysiological activity, with the positive voltage maximal near the centro-parietal region (Pz). Following the intervention, only the active-tDCS group displayed a marked enhancement in the intensity and spatial extent of this positivity in the parietal region, while the sham-tDCS and no-intervention control groups remained largely unchanged. The grand average waveforms (Figure 4B) visually demonstrate the increase in P3 amplitude for the active-tDCS group at electrodes Cz, CPz, and Pz.

Go trials: (A) Topographic maps display the voltage distribution within the 300–450 ms time window for the active-tDCS, sham-tDCS, and no-intervention control groups, pre- and post-intervention. (B) Grand average waveforms at the Cz, CPz, and Pz electrodes. The blue and red lines represent pre-test and post-test recordings, respectively. The shaded area indicates the P3 time window (300–450 ms).

To explore potential brain region specificity of the tDCS effect, we conducted a supplementary three-way repeated-measures ANOVA including the Electrode factor (Cz, CPz, Pz) (complete results in Table 3). This analysis reconfirmed the significant main effect of Time and the Time × Group interaction reported above. Additionally, the expected significant main effect of Electrode was observed, F(2,32) = 37.379, p < 0.001,  = 0.700, with P3 amplitude increasing from Cz (1.70 ± 0.24 μV) to CPz (2.92 ± 0.30 μV) to Pz (3.90 ± 0.29 μV). However, none of the interactions involving the Electrode factor (including Electrode × Group, Electrode × Time, and the three-way interaction) reached statistical significance (all