Low-beta frequency band Neurofeedback Training: Effects on Attentional Orientation, Executive Control, and Underlying Neural Mechanisms

doi:10.21203/rs.3.rs-7085583/v1

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Low-beta frequency band Neurofeedback Training: Effects on Attentional Orientation, Executive Control, and Underlying Neural Mechanisms

https://doi.org/10.21203/rs.3.rs-7085583/v1

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Neurofeedback Training (NFT) employs real-time feedback on brain activity to empower participants to self-regulate cognitive functions within specific brain regions. Understanding the underlying mechanisms of NFT is critically important, as these remain largely unclear, especially regard to their neural underpinnings. This study aimed to investigate whether, and to what extent, NFT bolsters attentional orientation and executive control in healthy adults, as well as to elucidate the neural mechanisms implicated in this process. Participants were divided into two groups: the neurofeedback group (NF, n = 19), who received real-time EEG signal feedback from the F3 electrode with training in the β1 band (15–18 Hz); and the sham group (sham, n = 18), who watched pre-recorded videos unrelated to brain activity. Both groups underwent pre- and post-intervention assessments, which encompassed attention tasks and EEG data collection. The NF group exhibited substantial improvements in attentional orientation and executive control performance compared to the sham group. These enhancements were corroborated in event-related potential (ERP) measures: the NF group demonstrated larger N1 amplitudes under attention-orienting conditions and larger N2 amplitudes under executive control conditions in the post-test. These findings imply that NFT can significantly enhance attentional orientation and executive control performance in healthy adults with substantial neural changes.

neurofeedback

attention orienting

executive control

β1

neural mechanisms

Attention is a fundamental cognitive function, conventionally delineated into three subsystems: alerting, orienting, and executive control. Orienting and executive control facilitate the efficient filtering of distracting information in complex and noisy environments, enabling individuals to focus on goal-relevant stimuli (Posner, 1980). Studies indicate that attention orienting is primarily associated with activity in the parietal and frontal lobes, whereas the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC) are integral components of the executive control network (Fan et al., 2005; Petersen & Posner, 2012). A pertinent inquiry arises: Could attentional control capacities be substantially enhanced by self-regulating neural activities of these relevant brain regions, particularly via neurofeedback training (NFT). The extent to which, and indeed whether, NFT can improve attentional control capacities remains a largely uncharted territory.

Beyond regional brain activity, attention-related event-related potentials (ERPs) also provide crucial physiological indices for understanding cognitive processing. Among these, the N1 component is an ERP elicited during early sensory processing of exogenous stimuli, typically occurring approximately 100 milliseconds after stimulus onset. It reflects early perceptual processing and is also closely associated with perceptual expectation and spatial orienting in attentional processes(Logemann et al., 2014; Marzecová et al., 2018). For example, under conditions with no spatial cues (i.e., high perceptual load), N1 amplitudes are significantly larger compared to conditions with spatial cues (i.e., low perceptual load) (Handy & Mangun, 2000). This suggests that the N1 component plays a critical role in the orienting of attention. The N2 component, which emerges between 200 and 350 milliseconds post-stimulus, is primarily associated with attentional state and cognitive control. Studies have shown that as the probability of No-go stimuli decreases, the amplitude of the N2 increases, with the most pronounced effects observed in the frontal cortex (Donkers & van Boxtel, 2004). Additionally, research using visual search paradigms has found that N2 is more sensitive to novel and mismatching stimuli, indicating its involvement in executive control processes and the suppression of irrelevant information during attentional tasks(Luck & Hillyard, 1994).

The β-band power within the prefrontal cortex (PFC) has demonstrated a significant correlation with attentional processes, notably encompassing orienting and executive control mechanisms (as evidenced by (Buschman et al., 2012a; Buschman & Miller, 2007a; Jurewicz et al., 2018; Putman et al., 2014). Research findings underscore the selective modulation of exogenous selection mechanisms by the β-band, particularly under circumstances of cognitive conflict (Dubey et al., 2023). As a result, β-band activity in the PFC is postulated to play a pivotal role in orchestrating top-down attentional control (as supported by Bardouille et al., 2010; Lee et al., 2016; Van Ede et al., 2011). Furthermore, investigations involving healthy individuals reveal a robust association between heightened local β-band amplitude and superior performance in attentional tasks(Buschman & Miller, 2007a; Wróbel, 2000).

The β-band spectrum can be further delineated into two distinct frequency ranges: β1 (15–18 Hz) and β2 (12–22 Hz). Notably, the β1 band, also referred to as the low β-band, has been specifically implicated in attentional enhancement phenomena as observed in neurofeedback studies (Egner & Gruzelier, 2001, 2004; Kober, n.d.; Schönenberg et al., 2021). Emerging evidence suggests that augmenting β1 activity can bolster inhibitory control mechanisms within the realm of attention(Grin’-Yatsenko et al., 2001). Research on alertness in attention has found that activity in the β1 frequency band of the left prefrontal cortex is significantly associated with changes in attentional alertness, suggesting that β1 activity in the left prefrontal cortex plays a critical role in the maintenance and control of attention(Kim et al., 2017). Moreover, the low β-band is intricately linked to generalized cortical activation, and targeted training within this frequency range has been demonstrated to mitigate errors in attentional tasks, expedite reaction times, and amplify P300 amplitude(Egner & Gruzelier, 2001). Nonetheless, the majority of existing research pertaining to β1 and attention has predominantly centered on individuals exhibiting diminished cortical activity, such as those diagnosed with ADHD, thereby casting uncertainty on the generalizability of these findings to healthy adult populations. Consequently, an exploration into the influence of low β frequencies on attentional processes and associated physiological modifications in healthy adults holds the potential to not only unravel novel insights into the neurobiological underpinnings of attention but also to inform strategies aimed at enhancing attentional capacities within educational and occupational contexts.

NFT stands out as a highly effective methodology for augmenting attentional capabilities(Egner & Gruzelier, 2001; Loriette et al., 2021; Rogala et al., 2016; Wang et al., 2013, 2015). Within the NFT paradigm, participants are furnished with real-time feedback on their cerebral oscillatory activity through a computerized interface, empowering them to exert conscious control over specific Electroencephalogram (EEG) rhythms or signal amplitudes within the cerebral cortex (Enriquez-Geppert et al., 2013; Rogala et al., 2016; Viviani & Vallesi, 2021). Empirical investigations involving healthy individuals have unveiled a robust positive correlation between heightened β-band power and superior performance in attentional tasks, with evidence suggesting that increased β amplitude in targeted brain regions correlates with enhanced task accuracy(Bekisz & Wróbel, 1993; Buschman & Miller, 2007a; Egner & Gruzelier, 2001; Vernon, 2005; Wróbel, 2000). Nevertheless, the majority of existing research endeavors have predominantly concentrated on specific β-band frequency ranges (12–15 Hz) or the ratios of β to α and γ bands, with relatively scant attention directed towards exploring other β sub-bands and conducting comprehensive analyses of the physiological processes underpinning this phenomenon(Ghaziri et al., 2013; Gruzelier, 2014). Consequently, further inquiry into the effects of NFT on alternative β-band frequencies is imperative to unravel the neural intricacies of attention and to identify evidence-based strategies for optimizing attentional performance.

The objective of this study is to enhance attentional orienting and executive control capacities through the implementation of NFT. To this end, healthy college students were randomly allocated to one of two experimental conditions: a genuine neurofeedback training (NF) group or a sham neurofeedback control group. Participants in both groups participated in an EEG-based NFT protocol specifically tailored to promote self-regulation of β1 power within the prefrontal cortex. Following the completion of training sessions, offline data analyses were conducted on datasets acquired prior to, during, and subsequent to the training period. These analyses were conducted with a dual purpose: firstly, to assess whether the NFT intervention yields improvements in attentional performance; and secondly, to shed light on the electrophysiological alterations that occur as a result of the training process.

Participants

Sample size was estimated using GPower 3.1 (Faul et al., 2009). For a repeated-measures ANOVA with two groups and five measurements, assuming a large effect size (f = 0.5), alpha = 0.05, and power = 0.80, the required sample size was calculated to be 17 participants per group. Thirty-seven healthy participants (18 females, aged 19–25 years; mean age = 20.6) took part in this study. They were right-handed (Edinburgh Handedness Inventory; Oldfield, 1971) college students with normal or corrected-to-normal vision. None of them had a history of neurological or psychiatric disorders. All participants provided written informed consent and were compensated for their time. All experimental procedures were approved by the Ethics Committee for Scientific Research of Shanghai University of Sport. Participants were randomly assigned to one of two groups: the genuine neurofeedback training (NF) group (n = 19) or the sham training group (n = 18). They remained blinded to their group allocation and whether they received real β1 feedback. The NF group received real-time neurofeedback based on their own β1 neural activity recorded at the F3 electrode site, whereas the sham group was presented with pre-recorded, random signals unrelated to their actual cortical activity.

Experimental Design and Procedure

Attention task

The experiment was conducted on a desktop computer, with stimuli presented on a 24-inch LCD monitor at a resolution of 1920×1080 pixels. Participants were seated at a distance of 60 cm from the screen, with their gaze fixed. E-prime 3.0 software was used for stimulus presentation and to record participants' response times (RT) and accuracy (ACC).

In this experiment, the stimulus materials were generated using the ImgInGrid-v1.3-release software, with each image sized at 650×900 pixels against a white background. In the center of each image, there was a 4×6 matrix of blue butterflies, blue ducks, and red butterflies, with an 80mm gap between each two adjacent animal objects. The blue butterflies serve as target stimuli, the blue ducks as non-target stimuli, and the red butterflies as distractor stimuli.

The experiment consisted of 608 trials in total, divided equally into four test sessions, with each session comprising 152 trials. Within each session, 104 trials with a red butterfly were categorized under the interference conditions, and the rest 48 trials without a red butterfly were allocated to the non-interference conditions. An equal number of 76 trials were allocated to the directed and undirected conditions, respectively. In directed conditions, there was an up or down arrow cue in the center of each stimulus picture, indicating participants to focus on only half of the animal objects above or below the cue only. In contrast, there was only a cross “+” in the center of each undirected trial, therefore, participants had to search across all animal objects before making a response. The sequence of trial presentation within each session was pseudo-randomized to guarantee a randomized and unbiased order. The cueing conditions were designed to assess participants' attentional orienting abilities, while the interference conditions were specifically tailored to evaluate their executive control of attention.

For each trial, a cue (a cross or up/down arrow) were first presented for 1000 ms, followed by a stimulus picture for 80 ms, and a blank (inter-trial interval, ITI) lasting for a randomized duration between 2000 ms to 3200 ms. The directional cues (up/down arrows) served to indicate the spatial location (either at the top or bottom) of the forthcoming target stimulus. Once the stimulus appeared, participants were required to identify the target's location (left or right) and respond by clicking the corresponding mouse button (left button for left, right button for right) as fast as possible. The flow of the experimental stimuli is shown in Fig. 1.

Neurofeedback protocol

NFT was carried out using a neurofeedback system developed by Thought Technology. This system employs an infinite impulse response (IIR) filter to extract frequency bands from raw EEG signals, while the brainwave activity data were amplified via the ProComp5 InFinity amplifier. Real-time feedback was presented to participants through a program, developed with the BioGraph InFinity software, which was employed to continuously display the participants’ brainwave activity in a dynamic and visually engaging format. In terms of electrode placement, the device was configured with three precision electrodes. One electrode was placed over the target brain region to capture the specific neural activity of interest, and the other two reference electrodes were positioned on the ears, ensuring accurate signal referencing and reducing potential noise interference. Before each session, electrode-scalp impedance was carefully adjusted and reduced to a level below 5 kΩ to ensure signal quality and reliability.

Participants were randomly assigned to either the NF group or the sham group. Both groups completed a one-week training program, consisting of five sessions. The key difference between the groups was that the NF group received real-time neurofeedback based on their own β1 signal from the F3 electrode, whereas the sham group received randomly generated signals unrelated to actual EEG activity. At the start of the training, participants familiarized themselves with the NFT program. Subsequently, they completed an attention task, during which the β1 power at the F3 electrode was recorded and averaged value was calculated (β1_mean). The individualized training target was set at β1_mean + SD. At the initial stage of training, the BioGraph InFinity software automatically set the training threshold at 80% of the baseline target power. Once the participant achieved a success rate of 80% in the first session—defined as the proportion of time during which the power exceeded the training threshold—the threshold for subsequent sessions was adjusted based on the individual's performance. Specifically, the threshold was modified within a range of 80–100% of the baseline. For example, if the participant reached an 80% success rate in the first session, the threshold for the next session was increased to 90% of the baseline, and so forth, until the participant reached a 100% success rate. If the success rate was lower than 80%, the baseline threshold remained unchanged(Chen et al., 2022). Given that participants generally improve their ability to regulate the target frequency band over time, such threshold adjustments continued dynamically throughout the training period (Cheng et al., 2015; Ros et al., 2013).

Each training session lasted approximately 30 minutes, with a 24-hour interval between consecutive sessions. Each session consisted of four rounds, each lasting 7 minutes and 40 seconds and comprising 20 trials. Within each trial, three distinct phases were incorporated. It commenced with a 2-second fixation screen, featuring a green circle that served as a visual prompt for participants to concentrate their attention. The subsequent 20-second feedback screen presented a purple bar graph, which visually represented the participant's β1 brainwave power. A yellow line on this graph denoted the training threshold. Participants were tasked with the responsibility of self-regulating their brainwave activity, aiming to elevate the bar graph above the established threshold. When successful regulation was achieved, a green light illuminated, accompanied by an auditory cue transmitted through headphones, signaling their accomplishment. The trial concluded with a 2-second blank screen, offering participants a brief respite before the commencement of the next trial. Furthermore, to alleviate potential fatigue, participants were granted a 3- to 5-minute break between sequences of trials.

Pre-/post EEG recordings and processing

Participants first completed a preliminary behavioral task followed by EEG data collection. The pre-test consisted of two sequences from the attention task, each lasting approximately 8 minutes, for a total duration of 16 minutes. After being fitted with EEG equipment, participants performed the attention task while their EEG signals were recorded in real time. A rest period was provided between the two sequences to minimize fatigue. Following the pre-test, participants underwent neurofeedback training. The post-test, conducted after the final training session, was identical to the pre-test.

EEG data were recorded using the BrainAmp Standard system (Brain Products GmbH, Germany) in conjunction with Brain Vision Recorder 2.0 software. EEG signals were acquired from 32 Ag/AgCl electrodes positioned according to the international 10–20 system, with an online sampling rate of 1000 Hz. The FCz electrode served as the online reference point, while the AFz electrode functioned as the ground. Vertical electrooculogram (VEOG) electrodes were placed 1 cm below the left eye, and horizontal electrooculogram (HEOG) electrodes were positioned 1 cm lateral to the outer canthus of the right eye. To ensure signal quality, electrode-scalp impedance was maintained below 10 kΩ prior to data collection.

Data analysis

Behavior Data. Key performance indicators included ACC and RT. Non-responses and extreme values—defined as RT shorter than 300 ms or exceeding the mean by more than three SD—were excluded from the analysis. ACC and RT were calculated for each participant.

NFT Data. Data preprocessing was carried out using the built-in system of the NFT program, during which training data exceeding ± 25 µV were excluded. Complete training data from each session for all participants were exported. To assess changes throughout the training process, we extracted the β1 band (15–18 Hz) amplitude data from the F3 electrode across the four segments of each training session and calculated the average value to represent the data at each time point.

Statistical analysis was conducted using SPSS 24. A 2 (Group: NF, sham) × 2 (Time: baseline, T5) repeated measures ANOVA was performed to examine differences in training effects between the two groups.

EEG Data. EEG data were preprocessed using Matlab (2019b) and the EEGLAB 13.6 toolbox. Preprocessing steps included electrode localization and the removal of unused electrodes. Data were re-referenced offline to the bilateral mastoids and filtered with a low-pass filter at 30 Hz and a high-pass filter at 0.1 Hz, with 50 Hz line noise removed. Eyeblink and movement artifacts were corrected using independent component analysis (ICA), and additional artifacts, such as those from head movements, were thoroughly excluded. The data were segmented using a 200 ms pre-stimulus baseline, followed by baseline correction. The analysis epoch extended to 1000 ms post-stimulus, based on participants' average RT. Trials with incorrect responses or amplitudes exceeding ± 100 µV were excluded from further analysis.

Based on a review of the literature, we focused on analyzing the N1 and N2 components. The N1 component reflects the rapid, intuitive response following stimulus presentation, whereas the N2 component is associated with conflict inhibition processing, predominantly observed in the frontal regions(Hillyard & Anllo-Vento, 1998; Lavric et al., 2004). Guided by topographical maps and previous research, we defined the N1 time window as 100–120 ms post-stimulus onset and analyzed the average amplitude at electrodes F3 and F4 (Luck et al., 2000). Similarly, the N2 time window was set at 250–300 ms post-stimulus onset, and the average amplitude at electrodes F3 and F4 was examined(Folstein & Van Petten, 2008).

Behavioral results

A 2 (Group: NF, sham)×2 (Time: pre, post)×2 (Interference: no-interference, interference) repeated measures ANOVA was conducted on ACC. There was a significant interaction between time and group (F (1,35) = 11.917, p = .001, η²_p = .254). Further simple effects analysis revealed that the ACC in the NF group was significantly higher in the post-test compared to the pre-test (p < .001), while no significant difference was found in the ACC between the pre-test and post-test in the sham group (p = .835). Similarly, a 2 (Group: NF, sham) × 2 (Time: pre, post) × 2 (Direction: directed, nondirected) three-way repeated-measures ANOVA was conducted on accuracy. The analysis revealed a significant interaction between measurement time and group (F (1,35) = 7.456, p = .010, η²ₚ = .176). Further simple effects analysis indicated that in the NF group, post-test accuracy was significantly higher than pre-test accuracy (p < .001).

A 2 (Group: NF, sham)×2 (Time: pre, post)×2(Interference: no-interference, interference) repeated measures ANOVA was conducted on RT. The results indicated that the interaction between time and group was significant (F (1, 35) = 5.382, p = .026, η²_p = .133). Further simple effects analysis revealed that the RT in the NF group was significantly faster in the post-test compared to the pre-test (p < .001), while no significant difference was found in the RT between the pre-test and post-test in the sham group (p = .835). Similarly, 2(Group: NF, sham)×2 (Time: pre, post)×2(Direction: directed, nondirected)three-way repeated measures ANOVA was conducted on RT. The results revealed a significant interaction between Measurement Time and Group (F (1, 35) = 5.301, p = .027, η²_p = .132). Simple effects analysis further indicated a significant difference in RT between the two groups in the pre-test (p = .014), but not in the post-test (p = .412).

The NFT effect

The analysis revealed a significant main effect of time (F (1, 35) = 10.472, p = .003, η²_p = .230). A significant interaction between group and time was also observed (F (1, 35) = 9.569, p = .004, η²_p = .215). However, the main effect of group was not significant (F (1, 35) = 1.056, p = .311, η²_p = .215). To further explore the interaction effect, independent samples t-tests were conducted separately for each group. The results indicated a significant difference in β1 power between the baseline and the final session in the NF group (t = -4.079, p = .001). In contrast, no significant difference was found in the sham group (t = -1.297, p = .212).

ERP analysis

The mean amplitudes of the N1 component at the F3 and F4 prefrontal sites were extracted and analyzed using a 2 (Group: NF, sham) × 2 (Time: pre-test, post-test) × 2 (Interference: no-interference, interference) repeated measures ANOVA. The main effects of group (p = .890), time (p = .807), and interference (p = .066) were not significant, and no significant interactions were observed (Fig. 6a and Fig. 7).

A 2 (Group: NF, sham) × 2 (Time: pre-test, post-test) × 2 (Direction: directed, nondirected) repeated measures ANOVA indicated that the interaction between time, group, and direction was significant (F (1, 35) = 6.731, p = .014, η²_p = .161). Further simple effects analysis showed that under the nondirected condition, the mean amplitude of the N1 component in the NF group significantly decreased from pre-test to post-test (p = .049), while the sham group showed no significant change (p = .116) (Fig. 6). Under the directed condition, there were no significant differences in N1 amplitude between pre- and post-test for either group (Fig. 6b and Fig. 7).

The mean amplitude of the N2 component at the frontal F3 and F4 was analyzed using a 2 (Group: NF, sham) × 2 (Time: pre-test, post-test) × 2 (Interference: no-interference, interference) repeated measures ANOVA. There was a significant interaction among time, group, and interference (F (1, 35) = 4.347, p = .044, η²_p = .110), while the time × group interaction was marginally significant (p = .051). Further simple effects analysis revealed that under the interference condition, the post-test N2 mean amplitude in NF group was significantly lower than in the pre-test (p < .001), whereas no significant change was observed in the sham group (Fig. 8a and Fig. 9).

A 2 (Group: NF, sham) × 2 (Time: pre-test, post-test) × 2 (Direction: directed, nondirected) repeated measures ANOVA indicated that the main effect of time was significant (F (1, 35) = 6.648, p = .014, η²_p = .160). The time × group interaction demonstrated a marginal significance (F (1, 35) = 4.093, p = .051, η²_p = .105). Further simple effects analysis indicated that under the nondirected condition, the post-test N2 mean amplitude in the NF group was significantly lower than in the pre-test (p = .002), whereas no significant change was observed in the sham group. Similarly, under the directed condition, the post-test N2 mean amplitude in the NF group was significantly lower than in the pre-test (p = .012), while the sham group showed no significant differences (Fig. 8b and Fig. 7).

This study examined the efficacy of NFT targeting the low-beta frequency band in enhancing attentional orienting and executive control in healthy adults, as well as the underlying neural mechanisms. Our findings support the initial hypothesis, as NFT significantly improved behavioral performance, particularly in attentional orienting and executive control, as reflected in changes in ERP components N1 and N2.

Behavioral Performance Enhancements

Behavioral data indicate that β1 NFT significantly enhances attentional performance in healthy adults. Specifically, participants in the NF group demonstrated a substantial increase in accuracy under both interference and non-interference conditions, accompanied by a reduction in RT following training. In contrast, the sham group exhibited no significant changes in attentional performance after the intervention. These findings are consistent with previous research suggesting that beta-band NFT modulates the neural substrates involved in attention regulation(Budzynski, 2009). Furthermore, beta-band activity has been linked to the activation of the visual system during heightened visual attention(Wróbel, 2000). Although research on β1-band training for attentional enhancement in healthy adults remains limited, our results reinforce the association between increased beta power and improved attentional performance.

Neurophysiological Correlates of Attentional Enhancement

Beyond behavioral improvements, the NF group exhibited significant neurophysiological changes, firstly, this was evident in the neurofeedback training data: compared to baseline β1 power, a significant increase was observed in the final training session of the NF group, whereas no such change was found in the sham group. This indicates that five sessions (T1-T5) of β1-band neurofeedback training over the course of one week effectively enhanced β1 power in the NF group. Concurrently, the NF group demonstrated significant post-training improvements in behavioral performance. These findings suggest that neurofeedback targeting the prefrontal cortex can enhance attentional performance—including both attentional orienting and executive control—through the upregulation of β1 frequency activity. Attentional selection is thought to involve interactions among prefrontal neurons. In the prefrontal cortex, cue-evoked local field potentials in the beta frequency band have been shown to reflect exogenous selection processes, corresponding to bottom-up modulation (Buschman & Miller, 2007b; Fiebelkorn & Kastner, 2021). Moreover, β-band activity following cue onset has been associated with endogenous selection and the suppression of sensory input during attentional tasks, indicative of top-down modulation (Antzoulatos & Miller, 2016; Buschman et al., 2012b). These findings suggest that beta activity may play a critical role in both exogenous and endogenous attentional selection. Although only a few studies have addressed this issue directly, some have reported that increases in β-band power in the prefrontal cortex following neurofeedback training are accompanied by improved performance in sequential learning tasks, demonstrating the impact of beta-band enhancement on attentional control(He et al., 2020). Our study extends this line of evidence by demonstrating that increases in β1-band power influence both exogenous attentional orienting and endogenous executive control.

These effects were also evident in changes in ERP components. Following training, participants in the NF group showed increased N1 amplitudes under undirected conditions and enhanced N2 amplitudes under interference conditions. These findings suggest that NFT facilitates early attentional processing, indexed by N1, as well as conflict monitoring and inhibitory control, indexed by N2.

Previous ERP research has consistently demonstrated that selective spatial attention modulates early sensory processing in the visual cortex, particularly influencing the P1 (70–130 ms) and N1 (150–200 ms) components(Clark & Hillyard, 1996; Hillyard & Anllo-Vento, 1998). In our study, the undirected condition, which imposed a higher perceptual load, elicited significantly larger N1 amplitudes compared to the directed condition. This result highlights N1 as a marker of attentional orienting capacity, as greater attentional resources were required to process unexpected stimuli in the absence of a cue.

Kober et al. (2015) further reported that NFT targeting sensorimotor rhythm (SMR) power led to a negative correlation between the N1 amplitude and SMR power in the training group, whereas the control group exhibited a weak positive correlation. These findings suggest that SMR training enhances stimulus processing efficiency, resulting in larger N1 amplitudes. Our results align with this conclusion, as participants in the NF group, who received authentic NFT, demonstrated improved attentional orienting under high perceptual load conditions, reflected in increased N1 amplitudes. In contrast, the sham group showed no significant changes. These findings extend the current understanding of low β NFT by highlighting its potential to enhance early sensory processing under cognitively demanding conditions. From a theoretical standpoint, this suggests that low β NFT may facilitate a more efficient allocation of attentional resources during early perceptual stages, particularly in contexts requiring high cognitive control. Practically, this provides a foundation for the application of lowβ-NFT in populations with attentional deficits, such as ADHD or age-related cognitive decline, offering a non-pharmacological intervention to improve cognitive performance.

Executive Control and the N2 Component

The N2 component, typically occurring between 200–350 ms post-stimulus, is widely recognized for its role in executive functions such as cognitive control and inhibition (Donkers & van Boxtel, 2004; Folstein & Van Petten, 2008; Moser, 2017; Nieuwenhuis et al., 2003). Specifically, N2 is sensitive to interference, stimulus complexity, and the proportion of non-target stimuli, reflecting processes related to conflict detection and inhibitory control. Research has demonstrated that frontal N2 activity, particularly during no-go trials, may serve as a neural marker of conflict monitoring or pre-motor inhibition at the response selection stage (Donkers & Van Boxtel, 2004). In the present study, N2 amplitudes increased under high-demand stimulus conditions (i.e., interference conditions), highlighting its sensitivity to cognitive conflict. Notably, participants in the neurofeedback (NF) group demonstrated a significant reduction in N2 amplitude from pre- to post-test, whereas no such change was observed in the sham group. This reduction suggests NFT enhanced the participants' ability to monitor and resolve cognitive conflicts more efficiently, thereby improving executive control performance. Supporting evidence from prior research, such as studies involving children with ADHD, also shows that theta/beta NFT interventions lead to significantly larger N2 amplitudes during no-go tasks in NF groups compared to controls (Bluschke et al., 2016; Heinrich et al., 2020), further underscoring N2 as a robust neural marker of executive function enhancement.

Our findings align with these prior studies, as N2 amplitudes in the NF group were significantly reduced under interference conditions post-training, indicating enhanced executive control. This effect was absent in the sham group, highlighting the specificity of NFT-induced improvements.

Limitations and Future Directions

Despite these promising findings, several limitations should be acknowledged. First, the relatively short training duration (five sessions over one week) may have limited the extent of neural changes that could be observed. Previous studies have shown that longer training periods (e.g., 10–30 sessions) are often necessary to achieve significant electrophysiological changes (Campos Da Paz et al., 2018; Cheng et al., 2024; Jurewicz et al., 2018). Second, the use of a single electrode (F3) may not have captured the full complexity of prefrontal neural activity. NFT effects are likely distributed across multiple brain regions, and future studies should consider using multi-electrode setups to better reflect these changes. Future research should address these limitations by extending the training duration and increasing the number of sessions to explore whether more robust neural and behavioral changes can be achieved. Additionally, incorporating multi-electrode EEG or combining EEG with other neuroimaging techniques (e.g., fMRI) could provide a more comprehensive understanding of the neural mechanisms underlying NFT(Yu et al., 2025). For example, fMRI could help identify changes in functional connectivity between the prefrontal cortex and other brain regions involved in attention and executive control.

This study provides compelling evidence that NFT targeting the low-beta (β1) frequency band can enhance attentional control in healthy adults, both behaviorally and neurophysiologically. Participants who received real-time β1 feedback exhibited significant improvements in attentional orienting and executive control, as reflected in increased accuracy, reduced reaction times, and modulated ERP components (N1 and N2). These enhancements were paralleled by increased β1 power over the left prefrontal cortex, reinforcing the role of β-band oscillations in top-down attentional modulation. Importantly, the observed changes in N1 and N2 components suggest that NFT not only improves early perceptual sensitivity to task-relevant stimuli but also facilitates more efficient conflict monitoring and inhibitory control under cognitively demanding conditions. Taken together, these findings extend current neurocognitive models of attention by demonstrating that β1-based NFT can upregulate both exogenous and endogenous components of attentional control in healthy individuals. From a translational perspective, this study highlights the potential of β1 NFT as a non-pharmacological intervention for enhancing cognitive performance in both normative and clinical populations. Future work should explore the long-term stability of these effects, optimize training protocols, and incorporate multimodal imaging techniques to further elucidate the underlying neural dynamics.

Ethics statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee for Scientific Research of Shanghai University of Sport (registration number 102772022RT079).

Data and Code Availability Statement

The data presented in this study are available from the corresponding author upon reasonable request.

Funding Declaration

This research was supported by the Humanities and Social Sciences Fund of the Ministry of Education (21YJA190013, 23YJAZH217).

Conflicts of Interest

The authors declare no conflicts of interest.

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Low-beta frequency band Neurofeedback Training: Effects on Attentional Orientation, Executive Control, and Underlying Neural Mechanisms

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