Impact of a Single Alpha Neurofeedback Session on Working and Visuospatial Memory in Football Players: A Comparison of Defenders and Attackers

doi:10.21203/rs.3.rs-6654210/v1

Working memory (WM) and visuospatial memory (VSM) are pivotal for rapid decision-making and effective teamwork in football. This study investigated the effects of a single-session alpha neurofeedback training (NFT) protocol at Pz on WM and VSM in 48 male players (aged 18–28), with a detailed analysis focusing on playing position, including both defenders and attackers, who were randomly assigned to either the NFT (n = 24) or sham (n = 24) groups. The NFT group received a 25-minute eyes-closed session to boost Peak Alpha Frequency (PAF) (7.5–12.5 Hz), while the sham group received non-contingent feedback. WM was assessed using an n-back task, and VSM was measured via a Football Memory Block-Tapping Test (FMBT). The NFT group showed significantly greater improvements in NCR-L1, NCR-L2 (WM), and FRS (VSM) than the sham group (p < 0.05), whereas no significant differences were found in RTs during the WM task or in BRS during the VSM task (p > 0.05). Moreover, defenders outperformed attackers on certain WM and VSM measures, potentially reflecting the distinct demands of their positions. These findings suggest that even a brief alpha NFT session may enhance certain cognitive functions in football players, offering a practical and time-efficient way to improve memory performance, with some variation possibly related to positional roles.

Neurofeedback

Memory

Sports

Athletes

Soccer

Playing Positions

Imagine a football (soccer) player near the penalty area as a lofted cross approaches. With a defender closing in, the player must quickly decide whether to control the ball with their foot, attempt a header, or redirect it to a teammate. Making the right decision requires quick perception and accurate execution under pressure, considering the ball’s speed, the defender’s pressure, and the teammate’s position. At the core of this process lies WM, the brain’s mental workbench, which retains and processes information briefly while adapting decisions in real-time. WM enables the player to prioritize relevant data and make rapid decisions under pressure (Bahameish & Stockman, 2024; In de Braek et al., 2019). Additionally, VSM, allows players to recall tactical patterns and movements, ensuring precise strategy execution on the field (Ben Mahfoudh & Zoudji, 2022).

To optimize this cognitive process, neurofeedback employs a closed-loop learning system in which the user and computer work together to adjust activity power in specific brainwave frequencies, potentially enhancing cognitive functions. The user learns to self-regulate brain activity in real time with visual or auditory feedback from the computer (Choi et al., 2023). One of the brain’s key rhythms is the alpha wave, which dominates the human EEG and peaks in the 7–13 Hz range. These oscillations, primarily observed in the occipito-parietal cortex, are essential for WM, enabling the brain to suppress irrelevant information and prioritize goal-relevant data. By enhancing selective filtering, alpha waves improve the efficiency and capacity needed for managing complex cognitive tasks (Escolano et al., 2014; Riddle et al., 2020). Alongside behavioral assessment and intervention tools, psychophysiological approaches are increasingly utilized in football, with a specific focus on enhancing alpha wave activity to optimize cognitive outcomes (van Boxtel et al., 2024).

While multi-session interventions have traditionally dominated NFT research in sports contexts (Corrado et al., 2024; Rydzik et al., 2023), single-session protocols are increasingly recognized for their practicality and efficiency. Despite being less common, these approaches have shown promising outcomes across both sports and non-sports contexts, highlighting their versatility in addressing diverse psychological and performance-related challenges. For example, Escolano et al. (2014) demonstrated significant cognitive enhancements following a single session of upper alpha NFT, while Gadea et al. (2020) reported notable reductions in anxiety through SMR based NFT. Similarly, Wu et al. (2024) highlighted the potential of a single-session intervention to improve putting performance in professional golfers. These findings underscore the viability of rapid NFT methods across various applications.

In football context, NFT research has largely focused on areas such as anxiety (Zadkhosh et al., 2018), behavioral issues (Conde et al., 2015), and cognitive functions (van Boxtel et al., 2024). While these studies offer valuable insights, they typically require multiple sessions over several weeks, posing challenges for athletes with demanding schedules. Addressing this gap, our study explores the feasibility of shorter, more flexible NFT methods that fit seamlessly into players’ routines. By employing brief, targeted interventions, we aim to determine whether single-session protocols can enhance cognitive factors, particularly before major matches. In this context, Assadourian et al. (2022) demonstrated that single-session alpha NFT significantly improved cognitive functions such as selective visuospatial attention in football players. One key difference between our study and previous research is the incorporation of playing positions, as we aim to investigate how field roles influence players’ responses to alpha NFT, specifically in WM and VSM.

To further support the effectiveness of NFT interventions in cognitive domains, previous studies reported significant improvements in WM (Da Silva & De Souza, 2021; Escolano et al., 2011; Gordon et al., 2020) and visual WM (Zhou et al., 2024), specifically targeting the Pz location, though these outcomes were achieved through multi-session protocols.

This study aimed to investigate the effects of a single-session alpha NFT on WM and VSM in football players. Unlike traditional multi-session protocols, it focused on a concise and adaptable approach designed to align with the demanding schedules of athletes. By examining the influence of playing positions, the study aimed to address gaps in the field and explore potential insights for developing practical and effective training strategies.

Participants

Forty-eight healthy skilled male football players, aged 18–28 years, were selected from the Tehran provincial league using convenience sampling. They were randomly divided into two groups of 24: NFT (n = 24; mean age = 25.8 years, SD = 1.37) and Sham (n = 24; mean age = 24.6 years,

SD = 2.06). Each group included 10 defenders and 10 attackers. Both groups followed the same experimental procedure, but the control group received sham feedback. All subjects were informed about the study's details and provided written consent. The inclusion criteria required players to have at least four years of national-level experience and no history of psychiatric or neurological disorders.

Design and Procedure

To ensure the sessions did not interfere with regular training, coordination was established with team coaches and subjects. All NFT sessions were conducted at the national brain mapping lab in Tehran. The 48 subjects were randomly assigned to two groups of 24: NFT (n = 24) and control (n = 24), with each group including 12 defenders and 12 attackers. The intervention involved a single session held in the morning. Before the session, subjects completed pre-training assessments for WM (n-back task) and VSM (FMBT). A three-minute baseline was recorded at the Pz location for both groups prior to the session. The NFT group received real-time neurofeedback targeting alpha wave activity, while the sham group received sham feedback. Subjects were not informed of the control group's placebo-based nature to ensure their motivation and effort remained consistent. Post-training assessments, including the n-back and FMBT tasks, were administered immediately following the session.

N-back Task

The n-back task, widely recognized as a reliable and valid tool for assessing WM, has demonstrated strong test-retest reliability for both 1-back and 2-back levels. Its effectiveness in evaluating WM and other cognitive abilities, such as fluid intelligence, is well-established. This task introduces a sequence of stimuli step by step. In this research, the 1-back and 2-back levels were utilized. In the 1-back level, subjects compare the current stimulus to the one presented immediately before; in the 2-back level, they compare it with the stimulus shown two steps earlier. Numerical stimuli (1–9) were presented for 650 ms with a 2-second gap between them. Subjects pressed buttons labeled "correct" or "incorrect" for matching or non-matching stimuli. Each test block lasted three minutes, and WM performance was assessed based on the Number of Correct Responses (NCR) and RT across two levels (L1, L2) (Jaeggi et al., 2010).

Football Memory Block-Tapping Test (FMBT)

We designed a visuospatial memory task for football players to make it more engaging and familiar. The task starts with a short practice phase in both forward and backward modes to help subjects understand how it works. This task is based on the Corsi Block-Tapping Test (CBTT) but adapted with football-themed elements. In the main task, football avatars are placed on a field, and a ball appears under one avatar’s foot in a sequence. Each ball stays visible for one second before disappearing. subjects need to memorize the sequence and repeat it by "striking" the ball’s location in the correct order (forward phase) or in reverse (backward phase). Sounds like cheering and ball kicks make the task more realistic and engaging. The sequence gets longer with each correct response, and the task ends after two consecutive mistakes or reaching the maximum trials. Scores are calculated based on the highest sequence completed correctly in both phases, and RTs are also recorded to measure speed and accuracy (Kessels et al., 2000).

Neurofeedback Training Protocol

NFT sessions were conducted in the laboratory in Tehran, using the single-channel ProComp2 device, manufactured in Canada. The protocol consisted of 25-minute sessions aimed at enhancing the PAF (7.5–12.5 Hz). Electrodes were attached with the active electrode placed at Pz, and the other electrodes positioned according to the 10–20 system, adjusted based on the subjects’ handedness, while maintaining all electrode impedances below 5 kΩ. Before initiating the main NFT protocol, subjects were asked to move their eyes, blink, and clench their teeth to verify the quality of the signal. A three-minute eyes-closed baseline was recorded to determine the IAF, and additional baselines were recorded before and after the session. The training range was set at IAF ± 2 Hz, and subjects were instructed to enhance alpha activity within this range to increase PAF. Following baseline recording, a 25-minute session with auditory feedback was conducted (Escolano et al., 2014). The eyes-closed protocol, chosen to enhance alpha waves by reducing visual input (Takabatake et al., 2021), utilized auditory feedback alternating between beeps and calming tones. When brain waves entered the target range, the beep stopped, and calming tones played, guiding subjects to maintain this state for as long as possible. For the sham NFT group, subjects underwent the same setup and procedures as the NFT group, but instead of receiving real-time feedback based on their own brain activity, they were provided with pre-recorded feedback from another subject.

Statistical Analysis

In the current study, three separate statistical analyses were conducted. First, paired t-tests were performed to assess within-group changes (Pre-training vs. Post-training) for both the NFT and sham-feedback groups. This analysis was applied separately to the WM and VSM measures, including NCR and RT at two levels (L1 and L2) for the WM subscales, as well as the FRS and BRS for the VSM measures. Second, independent t-tests were used to compare between-group differences (NFT vs. sham-feedback). Additionally, paired t-tests were conducted to evaluate changes in alpha peak before and after NFT within each group, followed by independent t-tests to compare these changes between the NFT and sham-feedback groups. All statistical analyses were performed using SPSS software version 24, with a significance level of α < 0.05.

Results

As presented in Table 1, NFT led to significant improvements in NCR-L1 (103.2 ± 1.31 to 106.8 ± 1.10), NCR-L2 (93.0 ± 1.54 to 97.6 ± 1.24), and FRS scores (56.0 ± 1.03 to 59.7 ± 0.86)
(p < 0.05), whereas no significant changes were observed in the sham group. Between-group comparisons further supported these findings, revealing moderate effect sizes favoring the NFT group. Although NFT participants exhibited slight decreases in reaction times (RT-L1 and RT-L2) after training, these changes did not reach statistical significance (p > 0.05), suggesting that NFT may have a more selective effect on cognitive domains related to memory rather than processing speed. No significant effects were found for BRS scores in either group.

Table 1 Pre- and post-training scores (mean + SEM) with paired-samples and independent t-test analyses for Neurofeedback Training (NFT) and sham groups, including Working Memory (WM) and Visuospatial Memory (VSM) subscales.

Test Scores	NFT (mean + SEM)		Sham (mean + SEM)		Paired-Samples T-Test		Independent T-Test
	Pre	Post	Pre	Post	NFT	Sham	Group
	Pre	Post	Pre	Post	p	p	t	p	Cohen’s d
NCR-L1 (Number) (WM)	103.2 (1.31)	106.8 (1.1)	100.5 (1.4)	103.3 (1.27)	0.041	0.144	1.852	0.046	0.55
NCR-L2 (Number) (WM)	93 (1.54)	97.6 (1.24)	90.9 (1.53)	93.3 (1.44)	0.031	0.278	2.263	0.029	0.65
RT-L1 (ms) (WM)	404 (8.31)	381.5 (7.42)	402.8 (8.14)	387.9 (7.73)	0.08	0.18	0.576	0.745	0.17
RT-L2 (ms) (WM)	442.4 (9.4)	420.6 (8.48)	451.2 (10.55)	441.2 (9.23)	0.1	0.295	1.643	0.109	0.47
FRS (Score) (VSM)	56 (1.03)	59.7 (0.86)	54.2 (1.08)	57 (1)	0.025	0.198	2.175	0.037	0.59
BRS (Score) (VSM)	45.6 (1.57)	49 (1.56)	45.9 (1.59)	48 (1.68)	0.103	0.392	0.491	0.627	0.12

Table 2 Independent t-test analyses for Neurofeedback Training (NFT) and Sham groups, including Working Memory (WM) and Visuospatial Memory (VSM) subscales.
Test Scores	NFT (mean + SEM)		Sham (mean + SEM)		Independent T-Test			Independent T-Test
	Defender (n = 12)	Attacker (n = 12)	Defender (n = 12)	Attacker (n = 12)	NFT			Sham
	Defender (n = 12)	Attacker (n = 12)	Defender (n = 12)	Attacker (n = 12)	p	Cohen’s d		p	Cohen’s d
NCR-L1 (Number) (WM)	108.5 (1.58)	105 (1.39)	104.7 (1.61)	102.1 (1.84)	0.108	0.67		0.184	0.43
NCR-L2 (Number) (WM)	100.3 (1.5)	94.9 (1.82)	94.4 (2.32)	92.1 (1.75)	0.029		0.93	0.46	0.32
RT-L1 (ms) (WM)	392.8 (11.56)	380.1 (9.7)	395.4 (10.72)	390.5 (12.79)	0.128		0.34	0.675	0.12
RT-L2 (ms) (WM)	431.53 (14.73)	416.62 (10.12)	447.94 (11.68)	440.45 (14.59)	0.241		0.34	0.472	0.16
FRS (Score) (VSM)	61 (1.47)	56.5 (1.61)	55.6 (1.7)	57.2 (1.74)	0.038		0.86	0.427	0.26
BRS (Score) (VSM)	51.3 (2.64)	48.8 (2.17)	48.7 (2.74)	47.3 (2.18)	0.194		0.29	0.662	0.16

Playing Positions (Defender and Attacker) Results

Table 2 compares the effects of NFT and sham-NFT on WM and VSM between two player roles: defenders and attackers. In the NFT group, defenders scored significantly higher than attackers on NCR-L2 (100.3 ± 1.5 vs. 94.9 ± 1.82; p = 0.029, d = 0.93) and FRS (61.0 ± 1.47 vs. 56.5 ± 1.61; p = 0.038, d = 0.86), suggesting that defenders may benefit more from NFT in in these cognitive domains. Although defenders showed numerically better scores on NCR-L1 and RTs, these differences were not significant. No significant differences were found between defenders and attackers in the sham group, indicating that the observed effects are specific to the NFT intervention.

Pz Analaysis

A paired t-test revealed a significant increase in PAF at Pz in the NFT group, from 9.72 Hz (SEM = 1.02) at pre-test to 10.28 Hz (SEM = 0.72) at post-test (p = 0.046), as shown in Figure 3. Additionally, an independent t-test was conducted to evaluate between-group differences. A significant difference in PAF was observed for the NFT group (p = 0.038, Cohen’s d ≈ 0.12). In contrast, the Sham group showed a more gradual increase, with a non-significant change from 9.62 Hz (SEM = 0.89) at pre-test to 9.8 Hz (SEM = 0.9) at post-test (p = 0.324). These results suggest that the NFT group experienced a clear enhancement in PAF following the intervention.

In more detail, Figure 4 compares PAF changes between the two playing positions (defender and attacker) within the NFT group. The defender position showed a significant increase in PAF, rising from 9.64 Hz (SEM = 0.96) at pre-test to 10.46 Hz (SEM = 0.73) at post-test (p < 0.05). However, there was no significant change in PAF for the attacker position, which increased from 9.80 Hz (SEM = 1.19) at pre-test to 10.1 Hz (SEM = 0.9) at post-test (p > 0.05). An independent t-test comparing the post-test results between the two positions showed no significant difference (p > 0.05).

This study examined the effectiveness of a single session of upregulating PAF at Pz on WM and VSM performance in football players, with particular emphasis on comparing outcomes between defenders and attackers. A sham-controlled study was designed using a brief 25-minute training procedure. The NFT group showed significant improvements in NCR-L1, NCR-L2, and FRS, and defenders outperformed attackers in the latter two measures.

In the WM domain, the NFT group outperformed the sham group across all scores, with significant between- and within-group improvements in NCR-L1 and NCR-L2, while the sham group exhibited only minor changes. Our findings align with those of Our findings align with those of (Escolano et al., 2014), as both studies demonstrate the positive impact of alpha based NFT on WM. Although the measures differed, with NCR-L1 and NCR-L2 used in our study and Paced Auditory Serial Addition Task (PASAT) in theirs, similar improvements in WM performance were observed. However, RT results were not aligned, as no significant changes in RT were found in our study, while elapsed time in PASAT showed significant improvement in their research. Despite the limited number of single-session NFT studies in general, evidence of rapid cognitive gains remains promising. For instance, MacDuffie et al. (2018) showed that just one fMRI-based NFT session boosted metacognitive awareness and supported CBT strategies. These findings highlight the potential of a single-session NFT to provide immediate cognitive enhancements, positioning it as a highly effective tool for football players during high-pressure training or crucial matches.

This results can be linked to the significant increase in PAF after a single session of NFT. PAF is critical for cognitive functions, as (Ghazi et al., 2021) highlighted its role in optimizing WM and attentional control. Similarly, Escolano et al. (2014), in a single-session NFT study, observed concurrent improvements in alpha power and WM, indicating a potential association between these measures. While similar improvements in WM after a single session have been observed in

non-athletic populations, the distinct cognitive demands of football, such as managing high-pressure situations, making rapid decisions, and adapting to constantly changing game dynamics (Vestberg et al., 2017), may contribute to enhanced responsiveness to NFT. In our study, RT did not significantly improve, likely due to the need for longer and more consistent training, as RT typically requires extended practice for meaningful neural adaptations (Zhao et al., 2013).

In addition to enhancing WM, NFT indicated potential benefits for FRS in VSM as well. The findings of (Escolano et al., 2014) align with our results, showing significant improvements in VSM performance after a single session of NFT. Although their study used a mental rotation task, and ours focused on FMBT, both demonstrate the effectiveness of NFT in enhancing cognitive functions in short-term interventions. Similarly, a single-session NFT study on football players reported similar results, showing improvements in peripheral visual attention (Assadourian et al., 2022). Although their study focused on a different area than ours, both emphasize the ability of NFT to enhance a range of cognitive skills. But, why did NFT significantly improve FRS but not BRS? One key reason is that forward recall aligns with the natural sequential processing of memory, making it inherently simpler and faster compared to backward recall. Backward recall, on the other hand, involves more cognitively demanding processes, as it requires reversing the sequence of stored information, which is less aligned with the typical structure of short-term memory encoding (Norris et al., 2019). Although we developed a new football-themed design task, likely boosted engagement and ecological validity, thereby improving performance in simpler components (BRS), the greater complexity of the backward recall phase may require additional NFT sessions to achieve significant gains.

Based on the results, defenders showed significant improvements in NCR-L2 (WM), FRS (VSM), and PAF compared to attackers after neurofeedback intervention. This advantage may stem from the specific demands of their role, which require continuous evaluation of threats and proximity to the ball. Defenders must constantly scan their surroundings to track the movements of both opponents and teammates, while maintaining awareness of the ball’s position. They rely heavily on motion cues, such as the actions of central attackers, to anticipate play patterns and predict potential threats. Moreover, defenders play a critical role in organizing their teammates and making positional adjustments, a process that requires rapid information processing and communication (Feist et al., 2024). Conversely, attackers showed slightly better RT, though not significant, which could be attributed to the nature of their role. Offensive players must continuously shift their attention across various elements during play, monitoring ball possession, scanning teammates' locations, and evaluating open spaces on the field. This distributed attentional pattern allows them to execute more effective offensive plays, including rapid passes to unmarked teammates, despite facing increased risks of mistakes in the process (Moreira et al., 2022). As a practical takeaway, coaches can enhance defenders’ scanning and communication skills, while encouraging attackers to refine quick decision-making and RT, potentially amplified by short neurofeedback sessions.

This study had two main limitations. First, the lack of control over external factors such as academic stress and seasonal variations may have influenced the cognitive performance of football players independently of the intervention. Second, genetic variability could have affected individual responses to the interventions.

Encouraged by these findings, we suggest that a single 25-minute alpha-based NFT session at Pz offers a simple and cost-effective method to enhance aspects of WM and VSM in football players. Increases in PAF were associated with improved task accuracy (WM) and memory order (VSM), particularly among defenders. While primarily targeting cognitive functions, combining NFT with physical training may yield complementary benefits, especially when tailored to the cognitive demands of different playing positions.

Funding: The authors did not receive support from any organization for the submitted work.

Conflicts of interest: The authors declare that they have no conflicts of interest.

Ethics approval: This study was approved by the Ethics Committee of the University of Tehran, under approval number IR.UT.SPORT.REC.1404.055

Consent: Informed consent was obtained from all participants included in the study.

Data availability: The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Author contribution:

H.G.Z was responsible for the conceptualization of the study, oversaw the implementation of the intervention, supervised the validity of the football-specific visuospatial memory task, and guided the overall analysis of the findings. H.B and F.A were involved in data collection and analysis, and prepared the initial draft of the manuscript. M.E developed the custom football-specific visuospatial memory task, contributed to study conceptualization, figure design, and the literature review. All authors contributed to and approved the final version of the manuscript.

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No competing interests reported.

Impact of a Single Alpha Neurofeedback Session on Working and Visuospatial Memory in Football Players: A Comparison of Defenders and Attackers

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Material and Methods

Participants

Design and Procedure

N-back Task

Football Memory Block-Tapping Test (FMBT)

Neurofeedback Training Protocol

Statistical Analysis

Behavioral Results

Discussion

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1