Oscillatory dynamics of motor learning across adulthood life span: a systematic review

Motor learning refers to a set of processes associated with practice and experience that are essential for acquiring new skills and adapting behavior throughout the lifespan. Mastery of motor skills plays a crucial role in maintaining autonomy and quality of life, particularly in aging populations. This learning process relies on internal neural mechanisms that lead to enduring changes in movement capability, yet the underlying functional and anatomical adaptations in sensorimotor circuits remain incompletely understood. These adaptations are often influenced by both task characteristics and age, highlighting the need for a deeper understanding of brain activity related to motor learning. In this pre-registered systematic review, we synthesized evidence from experimental studies and randomized controlled trials (RCTs) examining the relationship between motor learning and brain activities, specifically as measured by resting-state and task-related electroencephalography (EEG). We conducted a comprehensive literature search, identifying studies published in English between 2008 and May 2025 from PubMed, Scopus, and Web of Science databases and identified from web pages. After initial screening of 1,910 articles by title and abstract, a total of 80 studies met the eligibility criteria and were included in the final review. Studies were assessed for methodological quality in accordance with PRISMA guidelines. Our review focuses on EEG oscillatory activity across young, middle-aged, and older adults during motor skill acquisition, motor learning, adaptation and motor inhibitory control. We examined whether specific EEG features are linked to predicting motor learning performance, and explored how oscillatory patterns vary by task type, complexity, and age. By integrating findings across diverse studies, this review aims to advance our understanding of the neural mechanisms that support motor learning and its dimensions and inform the development of targeted, age-appropriate empirical research in healthy populations.

Systematic review registration: CRD42024569699.

1 Introduction

Motor learning involves enduring adjustments to bodily movements in a lifelong process of skill acquisition. This process includes factors such as practice and behavioral modification to overcome challenges encountered while executing actions in response to novel stimuli (Bracha and Bloedel, 2008). These challenges arise from interactions between e.g., the individual, the task, and the environment during motor activities, such as riding a bike or playing an instrument leading to permanent changes in skilled motor behavior (Krakauer et al., 2019; Lee and Schmidt, 2008; Leech et al., 2021; Masaki and Sommer, 2012; Magill and Anderson, 2017; Schmidt, 1988; Schmidt et al., 2018).

One interconnected concept related to motor learning is motor performance. Unlike motor learning, which refers to the long-lasting acquisition, refinement, retention, and improvement of motor skill behavior over time involving cognitive and neural processes, motor performance is defined as the ability to execute a motor task. It requires the integration of muscular and nervous system functions, and reflects the observable outcomes of movement. Motor performance is also a multifaceted state, dependent on distinct performance conditions such as force production, precision control, movement speed, resistance to fatigue, motor adaptation, and, finally, motor learning (Behrens et al., 2022; Forman et al., 2021), which serves as our inclusive term in this systematic review.

Another related concept is motor sequence learning, which refers to the process of acquiring and improving the execution of ordered motor actions through practice and training (Doyon, 2008; Gonzalez and Burke, 2018; Tzvi-Minker, 2015). It enhances both the speed and accuracy of performing learned movements. This type of learning involves predictive processing, allowing individuals to anticipate subsequent movements in a sequence. A key task used to study this phenomenon is the Serial Reaction Time Task (SRTT), which measures response times as participants learn sequences of actions. Brain regions such as the cerebellum and striatum are crucial for automating these tasks, while sequences of varying complexity engage distinct neural circuits. The short-term, immediate enhancements that result from repeated practice highlight practice effects, whereas skill acquisition refers to the comprehensive process of learning a new skill, from initial attempts to achieving proficiency.

Mastering skills through motor learning is underpinned by neurocognitive contributions and neurological processes involving brain activity and synaptic organization and working memory (Constantinidis et al., 2023; Dayan and Cohen, 2011; Mottaz et al., 2024; Rostami et al., 2009; Seidler et al., 2012). Learning-induced functional and anatomical changes within sensorimotor circuits are well-documented. For instance, studies on the explicit learning of sensorimotor tasks, as well as learning within sensory-motor circuits, establish a connection between action and the anticipated result. This suggests that brain states prior to movement can offer insights into the expected success of motor learning (Meyer et al., 2014; Zhou and Schneider, 2024).

Both cortical and subcortical regions contribute significantly to motor sequence learning, with cerebro-cortical and striatal-cortical networks playing a key role (Hikosaka et al., 2002; Doyon et al., 2002; Tzvi et al., 2014; Penhune and Doyon, 2002). Understanding these connections is essential for comprehending the mechanisms underlying motor learning, which is crucial for advancing motor learning enhancement and neurorehabilitation practices. However, due to genetic factors and individual differences in brain structure and function, the neurophysiology of motor learning remains not fully understood, whereas gaining knowledge in this area even can help predicting motor performance from resting neural markers (Tomassini et al., 2010; Herszage et al., 2020; Williams and Gross, 1980).

Several theoretical frameworks shaped our perception of motor skill acquisition and learning, such as Fitts and Posner Three-Stage Model, Bernstein's Degrees of Freedom Model, Gentile's Two-Stage Model, and Schmidt's Schema Theory. Contemporary frameworks and Modern Computational Models describe different motor learning mechanisms mapped onto specific neural regions, which are key for motor skill acquisition (Bernstein, 1966; Cano-de-la-Cuerda et al., 2015; Fitts, 1967; Gentile, 1972; Leech et al., 2022). What is less known and partially understood is the neural circuits engaged during skill acquisition that are modulated specifically by practice-based performance improvement, and those that predict recall performance. Moreover, the growing evidence suggests that brain activity during practice in the primary motor cortex and basal ganglia is associated with trial-by-trial practice performance which is predictive of immediate recall performance. These frameworks offer distinct perspectives on how new motor skills are acquired and refined, highlighting the need for further research to unravel how these neural activities translate into long-term skill retention, and expertise (Beroukhim-Kay et al., 2022; Cano-de-la-Cuerda et al., 2015).

Previous studies have highlighted the considerable roles of various brain structures in motor learning and skill acquisition. The prefrontal cortex (PFC) is involved in the cognitive processes required for mastering new motor skills (Grafton and Volz, 2019; Tian and Chen, 2021; Friedman and Robbins, 2021). The cingulate cortex facilitates motor control, error detection, performance evaluation, and the refinement of motor skills (Asemi et al., 2015; Paus, 2001). The primary motor cortex (M1) contains a somatotopic motor map corresponding to specific body part movements, which undergoes neuroplastic changes to accommodate new skills and enhance existing ones (Kandel et al., 2013; Papale and Hooks, 2017; Seidler, 2009; Tian and Chen, 2021). The supplementary motor area (SMA) is more involved in internally-generated movements is interconnected with M1 (Hardwick et al., 2012; Welniarz et al., 2019), the dorsal premotor cortex (PMd), and the ventral premotor cortex (PMC), facilitating the guidance of motor actions through sensory input especially externally-guided movements (Hoshi and Tanji, 2007; Kantak et al., 2011).

The basal ganglia and cerebellum support motor learning through complementary mechanisms that enable smooth, coordinated initiation and maintenance of movement (Baladron et al., 2023; Doyon et al., 2009; Torbati et al., 2024). The hippocampus also interacts with the striatum during motor sequence learning (Albouy et al., 2008, 2014). The coordinated activity of the primary somatosensory cortex (S1) and the posterior parietal cortex (PPC) supports motor movements through both somatosensory and visual feedback (Mirdamadi et al., 2025; Wang et al., 2024). Furthermore, major functional and anatomical networks including the basal ganglia, cerebellum, M1, SMA, premotor cortex, sensorimotor cortex, parietal cortex, right thalamus, cingulate gyrus, and putamen have been associated with motor deficits, spatial and sensorimotor learning, and motor sequence learning (Lefebvre et al., 2012; Penhune and Steele, 2012).

1.1 Age-related motor learning

Aging is associated with reductions in gray matter volume in key brain areas, including the primary motor cortex, somatosensory cortex, and cerebellum. These structural and functional brain changes can impact movement speed, coordination, and precision (Good et al., 2001; Salat, 2004; Seidler, 2009; Ward, 2003). Even in the absence of neurodegenerative disease, aging is characterized by alterations in sensorimotor activity, resource allocation, and cognitive-motor interactions, all of which affect perception, movement, and cognition (Seidler, 2009). While sensorimotor function declines with age, training and brain stimulation techniques have the potential to modulate these effects. Several studies have reported age-related declines in motor performance and motor learning (Brown et al., 2009; Durkina et al., 1995; Janacsek and Nemeth, 2012; Shimoyama, 1990). For example, simple repetitive tasks such as finger tapping show a reduction in frequency with age (Shimoyama, 1990). Likewise, older adults exhibit reduced learning rates in tasks like the pursuit of motor learning across multiple days (Durkina et al., 1995). Although immediate learning gains are often observed, the consolidation of motor memory is particularly affected by age (Janacsek and Nemeth, 2012).

Prior research has highlighted the importance of age-related differences in motor learning capacity and performance, often estimated through specific neural oscillatory bands such as mu and beta (Deiber et al., 2014; Liu et al., 2017; Rueda-Delgado et al., 2019). Few studies have directly investigated the impact of age on brain oscillations as predictors of motor learning improvements. However, existing evidence suggests that age-related changes in brain function, cognition, and motor abilities likely interact to influence learning outcomes (Espenhahn et al., 2019; Wang et al., 2019). Understanding these effects is critical for supporting functional independence in aging populations and for developing personalized sensorimotor training and rehabilitation protocols. This highlights the need to incorporate age, neural oscillatory patterns, and cognitive measures into motor learning research to capture a more comprehensive picture of individual differences across the lifespan.

Aging studies have consistently shown reduced processing efficiency, accompanied by declines in working memory and slower response times (Berchicci et al., 2012; Hedden and Gabrieli, 2004). Older adults generally require more cognitive resources for planning and executing motor tasks compared to younger individuals. Additionally, slower information processing and reduced attentional capacity can further hinder (Seidler, 2009). Given the centrality of aging in this context, this systematic review aims to examine how motor learning is shaped by age-related neural changes, specifically through EEG-measured oscillatory brain activity. We focus on motor skill acquisition and adaptation across the adult lifespan, highlighting neural patterns that may predict learning outcomes and inform individualized interventions for age-related motor deficits.

Examining both resting-state and task-related brain activities in the context of motor learning offers insight into how aging affects the ability to acquire and retain new motor skills. However, the precise effects of aging on motor skill acquisition, retention, and neural plasticity following practice remain inconclusive and merit further investigation. In this line, studies have shown that age affects both motor learning and associated alpha activity. It has been reported that the neural circuits involved in motor skill acquisition in older adults are like those in younger individuals, but older adults tend to exhibit more widespread activation patterns. This suggests that while the same networks are engaged, the efficiency of their use may differ due to age-related changes (Bootsma et al., 2021; Berghuis et al., 2019). There is also another study that investigated the effects of alpha-wave binaural acoustic beats on motor learning across different age groups. Their findings suggested that this type of stimulation could improve motor performance in older adults by enhancing alpha activity, thereby influencing their learning processes differently than in younger individuals (Herozi et al., 2024; Durand-Ruel et al., 2023). According to Park et al. (2025), resting-state oscillations are associated with age. The findings indicated that decreased alpha and altered beta activity with age provide foundational insights that relate to age-related changes in neural oscillations vary as a function of brain region and frequency band. Since these oscillations are known to influence neuroplasticity and motor performance, that is interesting to see how such brain oscillations change with age, which could influence motor learning processes (Park et al., 2025).

In this review, we synthesized current EEG research to better understand how age shapes motor skill acquisition, with a focus on oscillatory dynamics and neural plasticity. Specifically, we examined findings from studies using EEG to assess both resting-state and task-related activity, focusing on how age-related changes in e.g., alpha, beta, and mu rhythms relate to learning processes. By integrating results across studies, we aim to clarify how neural oscillations and functional connectivity evolve with age and how these changes shape the capacity to acquire and retain new motor skills. This approach allows for a more comprehensive understanding of the neural mechanisms underlying motor learning across the lifespan. Furthermore, it reveals important gaps in the literature, particularly in relation to age-specific variability in training outcomes. Additionally, these efforts would underscore the need for personalized approaches to motor rehabilitation and cognitive-motor interventions in older adults. In this context, the present review not only highlights patterns of compensatory brain activity associated with aging but also sets the stage for future research aimed at optimizing motor learning strategies through targeted neurophysiological markers.

1.2 Brain oscillations as a signature of motor learning

In recent decades, electroencephalography (EEG) has emerged as a non-invasive technique to measure neurological activity associated with motor tasks, offering insights into the brain mechanisms involved in the learning and adaptation of motor skills (Ahmadian et al., 2013; Hamada et al., 2023; Haar and Faisal, 2020a,b; Jee, 2021). Brain waves are classified into delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (13–30 Hz), and gamma (>30 Hz) frequency bands using the Fast Fourier Transform (FFT) technique. Each EEG frequency band is associated with distinct psychophysiological states. Studies suggest a correlation between EEG frequency band activity and the neural mechanisms underlying motor learning and task success (Teplan, 2002; Nayak and Anilkumar, 2025).

However, the results of individual studies remain inconclusive when considered alone. Across the literature, six EEG frequency bands have been associated with motor learning. Among these, beta (13–30 Hz) (Espenhahn et al., 2019), alpha (8–12 Hz) (Ghasemian et al., 2016), and theta (5–8 Hz) (Van Der Cruijsen et al., 2021) have been consistently identified as the most relevant for predicting and understanding motor actions. In addition, gamma (30–100 Hz) (Amo et al., 2015; Amo et al., 2017; Usanos et al., 2020), delta (2–4 Hz) (Hamel-Thibault et al., 2016; Wong et al., 2013), and mu bands (8–13 Hz) (Nakayashiki et al., 2014; Deiber et al., 2014; Zhang and Fong, 2019) are believed to offer valuable insights into predicting motor learning outcomes. These insights come from analyses of both task-based activity and resting-state functional connectivity (rsFC) (Sugata et al., 2020; see Table 1 for details).

Table 1

Table 1. Age-related findings across studies.

EEG power spectral density (PSD)—considered an indicator of motor learning which is linked to a range of brain functions. Spontaneous brain activity and PSD contribute to the encoding of information during motor learning, with alpha and beta bands specifically associated with motor performance (Hamada et al., 2023; Livne et al., 2022). In addition, PSD provides a robust framework for examining the neural correlates of motor learning and task success, making it an effective tool for understanding brain activity during motor tasks.

Research has demonstrated that frequency bands such as theta, alpha, beta, and gamma play distinct roles in visual attention and motor memory, indicating that PSD effectively captures these variations (Aliakbaryhosseinabadi et al., 2021; Hamada et al., 2023).

Beta oscillations are particularly sensitive to components of motor tasks involving top-down processing and sensorimotor behavior (Barone and Rossiter, 2021; Engel and Fries, 2010). These oscillations play a central role in motor learning, especially through their engagement with the primary motor cortex (M1) and brain connectivity with other brain regions. Beta-band activity has been shown to correlate closely with motor execution, preparation, and learning (Sugata et al., 2020). Notably, beta-band resting-state functional connectivity (rs-FC) predicts motor learning ability, with stronger beta connectivity between M1 and other areas correlating with better learning outcomes.

Beta activity is sensitive to the motor components of tasks. The so-called beta rebound, which is an increase in beta power exceeding resting levels and is a known marker of movement termination (Studer et al., 2010). During motor learning, average beta activity tends to decrease, while beta modulation related to motor tasks increases. This is accompanied by more pronounced synchronization/desynchronization volleys (Houweling et al., 2009). Baseline beta levels have been identified as predictors of subsequent learning and consolidation processes (Titone et al., 2022). For instance, a single session of practicing a pursuit-tracking motor skill was shown to reduce beta coherence between FC and Cz electrodes in young adults (Ghasemian et al., 2017).

Studies also suggested that cortical electrical activity is involved in movement execution during motor performance (Espenhahn et al., 2019; Jahanian et al., 2023c). Following visuomotor learning tasks, movement-related beta activity has been found to predict individual performance by 1 h, but not 24 h, after training (Espenhahn et al., 2019). Changes in beta-band connectivity during and after motor tasks are associated with motor memory consolidation, indicating that beta oscillations play a vital role in stabilizing newly acquired skills. However, individual variability exists as prior research revealed that higher baseline beta connectivity may correlate with poorer motor learning and weaker consolidation outcomes (Titone et al., 2022).

The stabilization of newly learned motor skills appears to rely on beta-band connectivity changes during and after learning akin to capturing and preserving a photograph of the learned movement for future recall. Yet, as with each musician in a band having unique strengths and weaknesses, individuals with higher baseline beta connectivity may sometimes face challenges in motor learning and adaptation due to the inherent characteristics of their neural networks (Watson, 2006; Aliakbaryhosseinabadi et al., 2021; Özdenizci et al., 2017; Peng et al., 2024). Overall, while beta-band oscillations are essential for motor learning, their specific roles may vary depending on the neural networks involved and individual differences.

Recent studies have also highlighted the importance of alpha activity patterns in motor learning. Alpha oscillations can influence the acquisition, retention, and efficiency of motor preparation (Ghasemian et al., 2016; Deiber et al., 2014).

Within motor sequence learning networks, functional decoupling in the motor-cerebellar loop has been observed by changes in alpha coherence between the premotor cortex and the cerebellum. Moreover, alpha activity has been shown to predict up to 60% of the variability in perceptual learning outcomes (Sigala et al., 2014; Schubert et al., 2020). These findings carry significant implications for rehabilitation strategies aimed at enhancing motor skills in individuals with movement disorders. Interventions such as neurofeedback targeting alpha suppression may be employed to increase cortical excitability and facilitate improved learning outcomes (Wan et al., 2014).

Theta oscillations (4–6 Hz) have been further shown to influence key aspects of motor skill acquisition, retention and are strongly associated with successful motor learning outcomes (Van Der Cruijsen et al., 2021). Additionally, Akkad et al. (2021) reported that enhanced motor skill acquisition was associated with increased theta-gamma phase-amplitude coupling, indicating that this can enhance non-hippocampal motor learning.

Although brain oscillatory activity has given useful understanding in measuring, and predicting motor learning outcomes across various tasks, less is known about the time course of training-related neural changes in alpha, beta, and theta bands, and how these changes interact with specific training parameters and moreover emphasizing brain network dynamics and inter-regional communication provides a clearer and more powerful understanding and prediction of motor learning and motor sequence learning outcomes than considering oscillatory activity alone (Dyck and Klaes, 2024; Mottaz et al., 2024; Takeuchi and Izumi, 2021).

Moreover, resting-state networks offer key insights into aging-related changes in brain dynamics. It is hypothesized that meaningful insights into predicting motor learning outcomes can be gained through the analysis of both task-based and rsFC. Despite numerous studies, questions remain about whether resting-state and task-related brain oscillations are linked to motor learning and can reliably predict short- and long-term effects of motor learning, and how these effects may vary with age. Prior research has shown that resting-state EEG can successfully predict motor learning in both clinical and healthy populations by characterizing baseline brain states and relating them to behavioral variability (Wu et al., 2014; Penalver-Andres et al., 2022). Thus, it is essential to examine whether resting-state EEG power can account for interindividual differences in motor performance and learning (Hübner et al., 2018; Imani and Godde, 2024a,b; Jahanian et al., 2023c; Özdenizci et al., 2016). The relationship between EEG activity at rest and during/after motor task execution can provide valuable insights into motor learning and motor sequence learning (Dyck and Klaes, 2024; Takeuchi and Izumi, 2021). Notably, there have been few studies regarding high alpha amplitude at rest that could predict alpha neuromodulation (e.g., alpha neurofeedback) has been found to predict learning success (Chikhi et al., 2023; Wan et al., 2014).

1.3 Current systematic review

Aging is associated with progressive changes in both cognitive and motor functions, which can significantly impact an individual's ability to learn and retain motor skills (Ren et al., 2013). Understanding the neural mechanisms that support motor learning across the adult lifespan is therefore critical, particularly in the context of designing interventions for age-related motor decline. The primary objective of this systematic review is to examine age-related differences in EEG-measured brain activity during motor learning. Specifically, we investigate how patterns of neural oscillations differ among young adults (18–35 years), middle-aged adults (35–55 years), and older adults (55–85 years) during motor skill acquisition, learning, and adaptation. By comparing these age groups, we aim to identify how aging influences the cortical dynamics that underlie motor learning processes.

This review focuses on studies utilizing both resting-state and task-related EEG recordings to capture oscillatory brain activity associated with motor performance. We seek to determine whether specific features of EEG oscillations measured either before or during training can predict individual learning outcomes. A further aim is to explore whether these neural signatures vary with task complexity and learning phase, and whether such changes are age-dependent.

We address several core questions:

• How do brain oscillations during motor skill acquisition correlate with training performance outcomes?

• Are resting-state and task-evoked brain oscillations associated with motor learning ability, and can they serve as predictors of learning outcomes?

• Do changes in cortical activity vary with task difficulty, and are they linked to training effects and the paradigms?

• Finally, are these neural changes and their relationships with motor learning age-dependent?

In addition, we examined whether changes in brain oscillations are task-specific and how neural activity adapts with increasing task complexity across different age cohorts. By synthesizing findings across these dimensions, this review aims to offer a comprehensive account of how motor learning is supported by EEG-measured brain activity throughout adulthood. Ultimately, this work addresses key gaps in the literature by integrating evidence on neural oscillations and motor learning across the adult lifespan. The insights gained are expected to advance our understanding of the neurophysiological basis of age-related motor learning differences and inform the development of tailored rehabilitation and training strategies for older adults experiencing motor impairments.

2 Materials and methods

2.1 Study selection and data collection

This systematic review has followed the standards of the PRISMA statement (Page et al., 2021a,b). To address the research questions, we considered the PICO format (Urrútia and Bonfill, 2010), and the review was prospectively registered on PROSPERO with the identification number CRD42024569699.

2.2 Search strategy

Authors independently searched the databases to find the relevant studies, using the PubMed, Scopus and Web of Science databases, and using the search terms in English (“EEG” OR “Electroencephalography ”) AND (“Brain oscillations” OR “brain networks” OR “rest state” OR “resting-state EEG” OR “task-based EEG”) AND (“Motor learning”) AND (“older adult” OR “young adult” OR “aging”).

2.3 Eligibility criteria

Studies were included if they met the following criteria:

(a) published in English, (b) published between 2008 and 2025, (c) the subjects of study were healthy young and older adults within the age range of 18–80, (d) the original RCT or experimental studies with parallel groups or cross over designs; (e) the intervention was single and multisession exercise; motor training, motor learning, or motor activity, motor planning, motor inhibitory control, motor performance improvement; and (f) the outcome was EEG activities in different age groups and based on different motor learning tasks. Studies were excluded if the language was non-English, case report studies, clinical trials, animal studies, or studies that used non-motor training interventions such as drugs or brain stimulation techniques. The search strategy for each used database is presented in Boxes 1–3.

Box 1. Search strategy for PubMed.

(“EEG” OR “Electroencephalography “) AND (“alpha” OR “alpha wave “ OR “alpha frequency” OR “alpha band activity” OR “alpha power” OR “alpha coherence” OR “alpha oscillations” OR “beta” OR “beta wave” OR “beta frequency” OR “beta band activity” OR “beta power” OR “beta coherence” OR “beta oscillations” OR “gamma” OR “gamma wave” OR “gamma frequency” OR “gamma band activity” OR “gamma power” OR “gamma coherence” OR “gamma oscillations” OR “delta” OR “delta wave” OR “ delta wave “ OR “delta frequency” OR “delta band activity” OR “delta power” OR “delta coherence” OR “delta oscillations” OR “theta” OR “theta wave” OR “theta wave “ OR “theta frequency” OR “theta band activity” OR “theta power” OR “theta coherence” OR “theta oscillations” OR “mu” OR “mu wave” OR “mu frequency” OR “mu band activity” OR “mu power” OR “mu coherence” OR “mu oscillations” OR “power spectrum” OR “power spectra density” OR “Brain oscillations” OR “brain networks” OR “coherence” OR “rest state” OR “rest-state” OR “resting-state” OR “resting-state EEG” OR “resting-state functional connectivity” OR “resting state power” OR “resting-state power” OR “task-based” OR “task-based EEG” OR “task related” OR “task-related” OR “task related power” OR “task-related power” OR “synchronization”[tw] OR “task related synchronization”[tw] OR “task-related synchronization”[tw] OR “task related desynchronization” OR “task-related desynchronization”) AND (“Motor learning” OR “Motor control” OR “Motor sequence learning” OR “Motor sequential learning” OR “Sensorimotor learning” OR “Motor imagery” OR “ Kinematics” OR “Motor training” OR “Motor practice” OR “Motor exercise” OR “Motor expertise” OR “Motor task” OR “skill acquisition” OR “Motor cortex”) AND (“older adult” OR “young adult” OR “aging”).

Box 2. Search strategy for SCOPUS.

TITLE-ABS-KEY ((“EEG” OR “Electroencephalography”)) AND TITLE-ABS-KEY ((“alpha” OR “alpha wave “ OR “alpha frequency” OR “alpha band activity” OR “alpha power” OR “alpha coherence” OR “alpha oscillations” OR “beta” OR “beta wave” OR “beta frequency” OR “beta band activity” OR “beta power” OR “beta coherence” OR “beta oscillations” OR “gamma” OR “gamma wave” OR “gamma frequency” OR “gamma band activity” OR “gamma power” OR “gamma coherence” OR “gamma oscillations” OR “delta” OR “delta wave” OR “ delta wave “ OR “delta frequency” OR “delta band activity” OR “delta power” OR “delta coherence” OR “delta oscillations” OR “theta” OR “theta wave” OR “theta wave “ OR “theta frequency” OR “theta band activity” OR “theta power” OR “theta coherence” OR “theta oscillations” OR “mu” OR “mu wave” OR “mu frequency” OR “mu band activity” OR “mu power” OR “mu coherence” OR “mu oscillations” OR “power spectrum” OR “power spectra density” OR “Brain oscillations” OR “brain networks” OR “coherence” OR “rest state” OR “rest-state” OR “resting-state” OR “resting-state EEG” OR “resting-state functional connectivity” OR “resting state power” OR “resting-state power” OR “task-based” OR “task-based EEG” OR “task related” OR “task-related” OR “task related power” OR “task-related power” OR “synchronization” OR “task related synchronization” OR “task-related synchronization” OR “task related desynchronization” OR “task-related desynchronization”)) AND TITLE-ABS-KEY ((“Motor learning” OR “Motor control” OR “Motor sequence learning” OR “Motor sequential learning” OR “Sensorimotor learning” OR “Motor imagery” OR “ Kinematics” OR “Motor training” OR “Motor practice” OR “Motor exercise” OR “ Motor expertise” OR “Motor task” OR “skill acquisition” OR “Motor cortex”)) AND TITLE-ABS-KEY ((“older adult” OR “young adult” OR “aging”)) AND PUBYEAR > 2007 AND PUBYEAR > 2007 AND PUBYEAR < 2025.

Box 3. Search strategy for WEB of SCIENCE.

TS=(“EEG” OR “Electroencephalography”) AND TS=(“alpha” OR “alpha wave “ OR “alpha frequency” OR “alpha band activity” OR “alpha power” OR “alpha coherence” OR “alpha oscillations” OR “beta” OR “beta wave” OR “beta frequency” OR “beta band activity” OR “beta power” OR “beta coherence” OR “beta oscillations” OR “gamma” OR “gamma wave” OR “gamma frequency” OR “gamma band activity” OR “gamma power” OR “gamma coherence” OR “gamma oscillations” OR “delta” OR “delta wave” OR “ delta wave “ OR “delta frequency” OR “delta band activity” OR “delta power” OR “delta coherence” OR “delta oscillations” OR “theta” OR “theta wave” OR “theta wave “ OR “theta frequency” OR “theta band activity” OR “theta power” OR “theta coherence” OR “theta oscillations” OR “mu” OR “mu wave” OR “mu frequency” OR “mu band activity” OR “mu power” OR “mu coherence” OR “mu oscillations” OR “power spectrum” OR “power spectra density” OR “Brain oscillations” OR “brain networks” OR “coherence” OR “rest state” OR “rest-state” OR “resting-state” OR “resting-state EEG” OR “resting-state functional connectivity” OR “resting state power” OR “resting-state power” OR “task-based” OR “task-based EEG” OR “task related” OR “task-related” OR “task related power” OR “task-related power” OR “synchronization” OR “task related synchronization” OR “task-related synchronization” OR “task related desynchronization” OR “task-related desynchronization”) AND TS=(“Motor learning” OR “Motor control” OR “Motor sequence learning” OR “Motor sequential learning” OR “Sensorimotor learning” OR “Motor imagery” OR “ Kinematics” OR “Motor training” OR “Motor practice” OR “Motor exercise” OR “ Motor expertise” OR “Motor task” OR “skill acquisition” OR “Motor cortex”) AND TS=(“older adult” OR “young adult” OR “aging”).

3 Results

3.1 Study selection criteria

The process of selecting articles for this review followed a systematic approach:

1. Initial identification: A comprehensive search was conducted to identify articles relevant to the topic. 1910 articles related to the subject were first identified.

2. Duplicate exclusion: Among the identified articles, 611 duplicates were automatically identified and excluded; 85 duplicate records were excluded by authors, resulting in 1214 unique articles.

3. Title and abstract screening: The titles and abstracts of the 1214 unique articles were meticulously assessed. As a result of this screening, 1094 were deemed not relevant to the review, and 122 were identified for potential inclusion.

Full text assessment

1. Accessibility confirmation: Following the title and abstract screening, accessibility to the full text of all 122 initially included articles was confirmed.

2. Comprehensive full text review: The full text of the remaining 122 was thoroughly reviewed. Adhering to the established inclusion and exclusion criteria, 42 were excluded, and 80 articles were selected for comprehensive review.

3.2 Data extraction

In the end, authors independently extracted relevant data from the studies included for this systematic review. This data encompassed various methodological and technical considerations, such as trial design, participant characteristics, experimental conditions, outcome measures, EEG parameters (e.g., frequency band, region of interest, EEG analysis method), and time point of measurement. Authors agreed about the extracted data based on the following PRISMA Flow Chart (cf., Figure 1).

Figure 1

Figure 1. The PRISMA flow chart 2020 statement (Page et al., 2021a,b).

3.3 Quality assessment

Authors independently assessed the methodological quality of selected studies using the ROB2 tool (Higgins et al., 2016; Sterne et al., 2019). Titles of these studies, and potential abstracts, were screened independently by both authors. Titles that contained any of the exclusion criteria were excluded based on the title only. Relevant full-text articles and full texts of abstracts that were inconclusive regarding their relevance were assessed, and studies that did not correspond with the inclusion criteria were excluded. Fitting articles were also extracted from reviews relevant to the topic and full-text article references. Data regarding the studies' designs were extracted. All study designs of any methodological quality were included. Due to our objective to perform a comprehensive data collection of the various parameters and measures, we did not factor in the strength of experimental evidence provided by the studies. In addition to studies that examined the efficacy of an intervention, we included studies that explored the feasibility of tools, hypotheses regarding mechanisms of learning, recovery, and the implementation of mathematical models. In such studies, assessment of the methodological quality would yield no benefit, due to their different objectives. A narrative synthesis of the literature was performed. Figure 2 illustrates the risk of bias assessment in all categories.

Figure 2

Figure 2. Risk of bias assessment (Higgins et al., 2016; Sterne et al., 2019).

3.4 Results

3.4.1 Evidence from prior research reviewed studies

Previous research has demonstrated that brain oscillations, functional connectivity patterns involved in motor control, and EEG power spectral density serve as important biomarkers associated with motor learning outcomes. These neural indicators reflect the activity and efficiency of motor neural circuits during both task-related and resting-state conditions. As such, they offer promising tools for diagnosing pathologies within motor-related brain areas, and guiding neuro-enhancement and rehabilitation strategies aimed at enhancing the acquisition of new motor skills. However, there remains ongoing debate regarding the utility of brain oscillations as reliable biomarkers, particularly in the context of age-dependent mechanisms and their application in designing targeted rehabilitation techniques.

In the current systematic review, a total of 80 studies were included. Of these, 67 were experimental studies, and 9 were randomized controlled trials (RCTs) (Allaman et al., 2020; Beik et al., 2020; Bootsma et al., 2020; Herozi et al., 2024; Schättin et al., 2016; Studer et al., 2010; Veldman et al., 2017, 2021; Zhang and Fong, 2019) and one semirandomized (Larsen et al., 2016). Additionally, three pilot studies were included (Baumeister et al., 2013; Penalver-Andres et al., 2022; Yang et al., 2017). The earliest included study dates back to 2010. Notably, interest in the relationship between brain oscillations and motor learning surged after 2018. A temporary decline in publications was observed between 2021 and 2022, likely due to disruptions caused by the COVID-19 pandemic. A detailed overview of all included studies is presented in Table 2.

Table 2

Table 2. Systematic review result table.

Across the reviewed literature, neural oscillations across several frequency bands, including alpha, beta, theta, mu, and gamma were found to correlate with improvements in motor learning. Many studies reported significant associations between EEG activity and behavioral performance during motor training, which are examined in more detail within the respective frequency-specific subsections of this review.

To better synthesize findings across diverse studies, we categorized all included studies by dominant EEG frequency band, even if the study's primary focus was on other factors such as age or recording condition (e.g., resting state vs. task-based EEG). This classification approach helps clarify how each band contributes to motor learning across various experimental conditions. A key theme emerging from several studies is the role of age as a potential modulator of motor learning success, with implications for e.g., peak performance, neurorehabilitation and AI assistive technologies. Our review highlights findings that explore whether age should be considered a crucial variable when developing interventions or when evaluating the robustness of motor learning across different life stages throughout basic or clinical research. For example, Christov and Dushanova (2016) investigated how aging affects brain performance by examining beta and gamma components of event-related potentials (ERPs) during an auditory discrimination task. They concluded that aging disproportionately impacts cognitive functions compared to sensory processing, with notable reductions in higher-frequency brain activity.

Deiber et al. (2014) examined mu and beta band activities during motor preparation and execution. Their findings emphasize age-related functional reorganization, with older adults displaying a shift in responsiveness from mu to beta frequencies. This supports the dedifferentiation hypothesis, which suggests that aging leads to less efficient use of neural resources and compensatory recruitment of additional circuits. Their results revealed increased beta-range activity and greater cortical activation during motor tasks, indicative of compensatory or reorganizational processes in older adults.

Further evidence from Yordanova et al. (2020, 2024) revealed that theta oscillations were phase-locked to motor response onset in both young and older adults. However, aging was associated with reduced midline frontal-central theta power, diminished functional asymmetry in theta synchronization, and slower reaction times, particularly during complex motor tasks (e.g., choice reaction tasks). These findings suggested a reorganization of motor theta networks in aging, characterized by increased intra-hemispheric and decreased inter-hemispheric connectivity.

In support of the alpha inhibition theory, Bönstrup et al. (2015a) found that elderly participants (aged 65+) exhibited no significant post-learning increase in alpha power in the primary motor cortices, unlike their younger counterparts. This points to a reduced capacity for inhibitory control in older adults, likely due to altered connectivity between frontal executive and sensorimotor networks. Notably, alpha-related inhibitory rhythms were enhanced following overnight consolidation in young adults but remained attenuated in older participants, suggesting diminished motor memory trace consolidation in aging.

Structural and functional brain connectivity also plays a critical role. For instance, Babaeeghazvini et al. (2018) examined how age-related differences in motor network connectivity (via resting-state and task-related EEG) relate to bimanual motor performance. Their study found that weaker structural connectivity between the dorsal premotor cortex (PMd) and primary motor cortex (M1) in the left hemisphere was associated with stronger—but less efficient—functional connectivity, ultimately correlating with poorer motor performance in older adults. These findings highlighted the importance of both anatomical and functional network integrity for effective motor learning across the lifespan.

3.4.2 Age, expertise, and neural dynamics in motor learning

Age-related changes in motor control have been extensively documented in the literature. Vieluf et al. (2018) specifically highlighted that aging is associated with a decline in the efficiency of neural control mechanisms, which often manifests as reduced force control capabilities. These findings are consistent with prior studies (Spedden et al., 2019; Roski et al., 2013), which showed age-related reorganization of sensorimotor networks. In particular, older adults frequently demonstrated greater cortical activation during simple motor tasks, suggesting compensatory recruitment of additional neural resources to offset declining motor efficiency.

Moreover, older adults tend to rely more heavily on cognitive resources during motor execution, leading to increased variability and greater task difficulty (Vieluf et al., 2018). Frolov et al. (2020) reported that elderly participants show significant delays in motor initiation, attributed to altered cortical activity patterns and increased sensorimotor coupling during the pre-movement phase. These findings underscore a shift from efficient motor planning to compensatory sensorimotor integration strategies with age.

3.4.3 The role of expertise in sensorimotor efficiency

Beyond age, motor expertise also plays a critical role in shaping neural control mechanisms. Vieluf et al. (2018) reported that individuals with expertise in fine motor tasks (e.g., precision mechanics) display enhanced force control and sensorimotor modulation compared to novices. These enhancements are likely underpinned by greater connectivity and coordination among motor-related brain regions, as observed in individuals with extensive training and task-specific experience.

Utilizing a dynamical systems approach, Morrison and Newell (2012) analyzed the variability in force control, identifying both deterministic and stochastic components contributing to performance differences across age and expertise. These insights emphasize the necessity of considering both age and experience when evaluating neural strategies in motor tasks.

3.4.4 Neural correlates of motor learning: EEG frequency bands

Studies utilizing EEG have consistently highlighted the importance of brain oscillations in motor learning, particularly in the beta frequency band (13–30 Hz). Beta activity is implicated in multiple facets of motor function, including preparation, execution, and post-movement states. For instance, event-related desynchronization (ERD) in the beta band precedes movement and reflects motor readiness, while post-movement beta rebound (PMBR) indicates the brain's return to a resting state (Abbasi and Gross, 2019; Haar and Faisal, 2020a,b). Research also links beta oscillations with feedback integration and error correction, especially in tasks requiring visual feedback (Barone and Rossiter, 2021; Davis et al., 2012). Meadows et al. (2016) demonstrated that beta suppression in the contralateral motor cortex correlates with faster reaction times, reinforcing its role in preparatory neural states.

Beta-band coherence has further been associated with functional connectivity and motor inhibition control. Wu et al. (2014) identified beta oscillations (20–30 Hz) as strong predictors of motor skill acquisition, outperforming other bands such as theta, alpha, and gamma. Moreover, Ding et al. (2023) suggested that elevated beta power correlates with diminished motor inhibitory control.

In the context of resting-state functional connectivity, Sugata et al. (2020) found significant correlations between beta-band rs-FC and motor learning capabilities, particularly involving the M1 seed region and other motor-relevant brain areas. By contrast, alpha-band rs-FC did not show such associations.

3.4.5 Beyond beta: other relevant frequency bands

Although beta rhythms dominate the motor learning literature, other frequency bands also contribute to motor learning:

• Delta Band (0.5–4 Hz), though less studied, delta oscillations are implicated in motor planning and directional control, possibly modulating higher-frequency activity (Hamel-Thibault et al., 2016).

• Theta Band (4–8 Hz) is particularly relevant for implicit sequence learning and motor-cognitive integration (Schättin et al., 2016; Van Der Cruijsen et al., 2021; Yordanova et al., 2020, 2024). Theta activity at midline-frontal and parietal sites has been linked to enhanced learning and cortical plasticity.

• Low Frequency Bands (2–5 Hz), Anwar et al. (2016) identified this band using EEG-EMG coherence, linking it to activity in the PPC, MFC, and PFC during finger movement tasks. Moreover, prior research (Muthuraman et al., 2012) has shown that corticomuscular coherence between bilateral cortical areas is crucial for coordinating bimanual movements. Different motor rhythms, particularly in the beta frequency band, exhibit interhemispheric coherence and generate synchronized motor activity that enables effective communication between the two hemispheres. This synchronization supports both the execution and learning of bimanual movements by facilitating interhemispheric communication necessary for smooth and coordinated actions. Importantly, such coherence is dynamically modulated during task execution, reflecting the changing demands of coordination between the two hands. Alpha Band (8–12 Hz), associated with attention and sensorimotor integration, alpha activity has been shown to predict motor-skill acquisition (Allaman et al., 2020; Rosjat et al., 2024). Alpha modulation is often observed during both task-related and resting-state conditions and is crucial for understanding changes in connectivity during motor learning (Bootsma et al., 2020; Mottaz et al., 2024).

• Mu Band (11–14 Hz) is closely related to beta, mu rhythms are prominent in sensorimotor regions and show ERD/ERS patterns during motor tasks (Nakayashiki et al., 2014; Deiber et al., 2014). These bands are essential in motor preparation and feedback processing.

• Gamma Band (30–100 Hz): Despite limited findings, gamma rhythms may play a role in fine motor control and coordination (Amo et al., 2015, 2017; Hamada et al., 2023; Usanos et al., 2020).

3.4.6 Oscillatory modulation through external training

Recent studies explored methods to enhance motor learning through external modulation:

• Visuo-Tactile Feedback: Jahanian Najafabadi et al. (2023) found that multisensory integration improves motor learning, with differential patterns of alpha, beta, and theta activity in older vs. younger adults.

• Binaural Acoustic Beats (BAB): Herozi et al. (2024) demonstrated that alpha BAB enhances motor performance by increasing oscillatory activity across different bands in young and older adults, albeit with distinct neural signatures.

From these studies, we learned that the extensive body of research on brain oscillations reveals their critical relevance as biomarkers for motor learning prediction, highlighting how various EEG frequency bands especially beta, alpha, theta, mu, and gamma reflect the neural dynamics underlying motor skill acquisition and control. Beta oscillation (13–30 Hz), has received most of its attention in the motor learning literature, being strongly linked to motor readiness, execution, feedback integration, and post-movement states, with changes in beta power correlating with motor performance and plasticity. Age has a substantial impact on motor learning-related neural circuits; older individuals typically show less flexible reorganization (in alpha and beta bands), greater levels of beta power during rest, changing connectivity in the motor network, and reliance on compensatory recruitment of neural circuits, resulting in poorer motor efficiency when learning a new task, and generally slower learning. Theta oscillations play a role in motor-cognitive integration and implicit learning, while alpha rhythms are crucial for attention and sensorimotor integration, with reductions in alpha power post-learning and during consolidation noted in older adults, with moderate gains also reported diminishing by experience. Other neuromodulation approaches such as visuo-tactile feedback and alpha binaural acoustic beats have shown promise in enhancing motor learning by modulating these oscillatory activities, additionally such interventions can yield different results across age groups. This integrated evidence supports the utility of brain oscillations as predictive markers of motor learning capability and provides the necessary groundwork to support the development of age-sensitive interventions for neurorehabilitation and the use of neuro-enhancement strategies to support optimal motor skill acquisition throughout our lifespan.

3.4.7 Conflicting and converging findings across studies

The literature on oscillatory brain activity and motor learning has been inconsistent with respect to its ability to enhance motor learning through oscillatory brain activity. For example, one set of studies noted that changes in local oscillatory power, specifically in the theta and high-gamma phase-amplitude coupling, were essential during motor skill acquisition, emphasizing task-specific modulation within motor cortical areas. Others argued that local oscillatory power changes are not adequate as predictors for subsequent motor learning. Instead, those studies emphasized the dynamic interconnectedness of large-scale functional connectivity dynamics across alpha and beta bands spanning multiple brain regions, including the motor cortex, striatum, and medial temporal lobe (Mottaz et al., 2024). Furthermore, there is variability across studies on the oscillatory frequency bands that are included. For example, beta oscillations can be specific to the maintenance of motor states and thus are always associated with motor learning (e.g., Dyck and Klaes, 2024; Khanjari et al., 2023; Sallard et al., 2014; Matta et al., 2025). Whereas, other studies suggested that the motor learning process itself included either alpha or theta bands only (Studer et al., 2010; Van Der Cruijsen et al., 2021). The variances from prior studies might be due to differences in the experimental paradigms, neural recording modalities, and participant populations.

Although inconsistencies persist, there is some agreement when the studies were put into the context of certain contextual factors such as motor task type, or their research purpose. Research that employs simple tasks like finger-tapping, or serial response paradigms generally emphasize the role of cross-frequency coupling (e.g., theta–high gamma) specifically focused within the motor-related cortical areas during the preliminary stages of learning (Dürschmid et al., 2014). More complex forms of motor sequence learning tasks typically engage interactions not only in local oscillatory activity, but also network-level interactions, specifically functional connectivity in the alpha and beta frequency bands. In addition, the researchers' purpose of research was to assess immediate motor performance, long-term consolidation, or discovery of underlying neural mechanisms shapes which oscillatory features are most notable. In some studies, authors emphasized on using clinical populations, while applying clinical interventions (inducing frequency band modulation) like transcranial alternating current stimulation (tACS) or fMRI to present changes in performance or symptoms. The results of these studies may also vary due to clinical heterogeneity (Takeuchi and Izumi, 2021).

3.4.8 EEG studies by age group and frequency bands

In Table 1, we present a summary of studies categorized by the frequency bands explored. Of the 80 studies reviewed, 45 focused on participants in the young age group (18–35 years), 7 of those are in the middle-aged group (35–55 years), and 3 from the older age group (55–85 years). Additionally, 25 studies included participants from both the young and older age groups. See Figures 3, 4 for graphical information and Table 1 for detailed frequency bands per age group reported by reviewed studies.

Figure 3

Figure 3. Age group distribution in motor learning studies.

Figure 4

Figure 4. Frequency band distribution in motor learning studies.

3.4.9 Age-related findings across studies

Table 1 summarizes studies that compared age-related data across three groups: Young Adults (YA; 18–35 years), Middle-aged Adults (MA; 35–55 years), and Older Adults (OA; 55–85 years). Abbreviations: YA = Young Adults; MA = Middle-aged Adults; OA = Older Adults.

3.4.10 Reviewed studies across adulthood life span

In the following table, we present participants' demographic, task type, frequency bands and other related parameters and results reported by each study. Therefore, participants age and gender demographics indicated as: TN = total number of participants, F = female, M = male, AR = age range, yrs = years, YA =young adults, OA = old adults.

4 Discussion

In this systematic review, we synthesized findings from 80 experimental studies examining the relationship between motor learning and brain oscillatory activity, with a specific focus on age-related differences across the adult lifespan. Our objective was to clarify how resting-state and task-based neural oscillations relate to motor skill acquisition, retention, and adaptation, and whether these patterns vary meaningfully with age. Building on the hypothesis that aging modulates the neural mechanisms underlying motor learning, we aimed to identify oscillatory markers that may predict individual learning outcomes and offer insights into compensatory processes in older adults. Overall, the evidence reviewed highlights several consistent patterns. First, motor learning is preserved in older adults, though modulated by age-related changes in neural efficiency and connectivity. Second, beta and alpha oscillations consistently emerge as critical predictors of motor learning across age groups (Dyck and Klaes, 2024; Mottaz et al., 2024). Third, the engagement of broader cortical networks in older adults suggests compensatory recruitment rather than fundamental deficits. These findings suggest that despite reduced processing efficiency, the aging brain retains a remarkable capacity for adaptation (Van Ruitenbeek et al., 2022).

A growing body of research has investigated how aging alters cortical dynamics during motor learning. Declines in motor performance with age are often attributed to less efficient neural communication and changes in functional connectivity within motor control networks. Notably, older adults tend to experience greater performance drops as task demands increase (Seidler et al., 2009; King et al., 2013, 2017; Bootsma et al., 2021; Herozi et al., 2024; Rueda-Delgado et al., 2019). However, despite these challenges, the ability to acquire new motor skills is largely preserved in later life. This suggests that age-related plasticity remains active and may be supported by alternative or compensatory neural strategies. Factors such as task complexity, feedback type, and learning conditions appear to influence how motor learning unfolds in older adults (Veldman et al., 2021; Sallard et al., 2014), underscoring the need to account for these variables when interpreting age-related differences in motor learning performance.

Brain oscillations measured through EEG are often associated with learning and neuroplasticity. However, it is important to recognize that these oscillations are correlated —not directly measures— plastic changes. While oscillatory activity may reflect processes involved in learning and adaptation, its precise role remains an area of active investigation. Beta oscillations (13–30 Hz) are especially important in motor learning. Typically seen in sensorimotor and frontal areas during wakefulness, beta rhythms are thought to support top-down processing, sensory integration, and the maintenance of current brain states (Engel and Fries, 2010; Spitzer and Haegens, 2017). They play a key role in motor preparation and execution, as well as in integrating sensory feedback. Multiple studies have identified beta oscillations as central to motor learning, especially in the frontal, parietal, and temporal lobes, as well as the basal ganglia and sensorimotor cortex (Mottaz et al., 2024; Hamada et al., 2023; Espenhahn et al., 2019; Barone and Rossiter, 2021).

The beta rebound—an increase in beta power following movement completion—serves as a marker for movement termination (Studer et al., 2010). During motor learning, baseline beta activity tends to decrease, while modulation in response to motor tasks becomes more dynamic (Boonstra et al., 2007; Houweling et al., 2009). Baseline beta power has even been proposed as a predictor of subsequent learning and consolidation processes (Titone et al., 2022). Some studies have also observed reduced beta coherence after training (Ghasemian et al., 2017). While several investigations have noted increased beta power at rest in older adults (Heinrichs-Graham et al., 2018), others found no significant age-related differences (Babiloni et al., 2006). These patterns may reflect age-related compensatory mechanisms which have been attributed to the change in alpha and beta patterns in aged participants, wherein this age groups engage additional motor and prefrontal resources to maintain performance, aligning with the Scaffolding Theory of Aging and Cognition (STAC, Park and Reuter-Lorenz, 2009; Knights et al., 2021; Derya and Wallraven, 2024). Advancing age has been associated with changes in the default mode network (Duda et al., 2019), characterized by increased activation in frontal brain regions and reduced activation in posterior areas. This shift is commonly interpreted as a compensatory mechanism to counteract age-related decline in specific neural systems (Reuter-Lorenz and Park, 2010). Such neural alterations significantly influence functional reorganization in older adults compared to younger individuals, resulting in modified patterns of cognitive, sensory, and motor processing. These changes can impact performance across a range of everyday activities, including tasks that rely on motor learning (Bernard and Seidler, 2012; Reuter-Lorenz and Park, 2010). These findings support the perspective offered by the STAC, which suggests that older adults rely on compensatory neural processes, both at the physiological and functional levels, to sustain cognitive and motor abilities despite age-related decline (Goh and Park, 2009; Reuter-Lorenz and Park, 2014).

These findings are further aligned with broader models of cognitive aging, such as the Hemispheric Asymmetry Reduction in Older Adults (HAROLD, Cabeza, 2002), which emphasize compensatory recruitment and neuroplastic adaptation. Incorporating EEG-based markers into these frameworks may enhance their utility in predicting who benefits most from specific interventions. As we move toward a precision rehabilitation paradigm, integrating neural, cognitive, and behavioral data will be key to designing scalable, evidence-based interventions for aging populations.

Reductions in frontal delta (0.5–4 Hz) activity have been observed following motor training in young adults (Mak et al., 2013; Wong et al., 2013), and aging is associated with decreased delta activity in occipital areas (Babiloni et al., 2006). Delta activity is associated with internal cognitive processing and decision-making. During externally focused tasks, delta power tends to decrease (Giannitrapani, 1971; Babiloni et al., 2017). Other frequency bands also play key roles. Theta oscillations (4–7 Hz), especially in the frontal cortex, are linked to memory, cognitive control, error monitoring, and conflict resolution (Cavanagh et al., 2009; Cohen et al., 2008). In motor learning, theta power often increases in later stages, especially in parietal and frontal regions (Perfetti et al., 2011; Pitto et al., 2011). Alpha rhythms (8–13 Hz), dominant during relaxed wakefulness, are involved in sensorimotor integration and cognitive effort (Crone et al., 1998). Alpha desynchronization is typically associated with improved performance. While some studies suggest alpha power declines with age (Markand, 1990; Klimesch, 1999), others find this decline only in individuals with cognitive impairments (Jelic and Kowalski, 2009; Babiloni et al., 2006). During motor learning, alpha power has been shown to increase in successful learners (Haufler et al., 2000; Karabanov et al., 2012). Increased alpha desynchronization and theta power in older adults may represent greater cognitive effort during learning, consistent with the idea of neural inefficiency or dedifferentiation in aging. These dynamics underscore the shift from automatic to more effortful motor control strategies in older age.

Reduced alpha power over the sensorimotor cortex has been linked to increased cognitive load, and heightened attentional demands, particularly during intensive (massed) practice in visuomotor learning tasks (Studer et al., 2010). This suggests that when the brain is more actively engaged in processing motor tasks, alpha activity diminishes in regions responsible for sensorimotor integration. Additionally, research has shown that lower levels of neural activity across several brain areas can be associated with more rapid motor learning, implying that efficient learning may involve more focused or economical neural processing (Gehringer et al., 2019). In contrast, aging appears to affect brain oscillations differently. An observed increase in beta band power among older adults may be connected to elevated levels of gamma-aminobutyric acid (GABA) transmission, which plays a key role in inhibitory control within the motor system (Gehringer et al., 2019; Heinrichs-Graham and Wilson, 2016). These patterns reflect age-related neurophysiological changes that can influence motor learning and performance.

The mu rhythm (10–13 Hz), overlaps with the alpha band but is focused on sensorimotor areas and is modulated during movement observation, execution, and imagery (Marshall and Meltzoff, 2011; Pineda, 2005). Mu suppression is a reliable indicator of motor system engagement and has been observed during motor learning (Alhajri et al., 2018).

Effective connectivity, functional connectivity or dynamic communication between brain regions, is crucial for understanding motor learning. Studies have shown that strong connectivity, particularly in alpha-band phase synchronization predicts the response amplitude of the distant brain regions effectively connected to M1 (McGregor and Gribble, 2017; Tomassini et al., 2010; Zazio et al., 2021). Increased functional connectivity within task-relevant networks is associated with more efficient motor performance (Heitger et al., 2012). Resting-state connectivity in the alpha and beta bands has been linked to offline learning and motor adaptation (Manuel et al., 2018; Özdenizci et al., 2017). Moreover, prior research reported gamma oscillations (>30 Hz) are linked to higher-order cognitive processes such as attention, perception, and motor control (Uhlhaas et al., 2010). Increases in gamma activity have been observed during motor execution and imagery, and also after training (Crone et al., 1998; Perfetti et al., 2011; Amo et al., 2017). These increases are thought to reflect the engagement of local cortical networks involved in fine-tuning motor commands and integrating sensorimotor information. Furthermore, enhanced gamma activity following motor training suggests a link between motor learning and cortical plasticity. As individuals practice and refine motor skills, the heightened gamma response may represent more efficient neural synchronization and improved functional connectivity in task-relevant areas. This supports the idea that gamma oscillations not only accompany motor actions but may also play an active role in the consolidation and optimization of motor performance over time.

Together, these findings highlight the importance of brain oscillations in motor learning, particularly as they relate to age-related decline and rehabilitation. Incorporating these neural markers into therapy and training programs could lead to more personalized and effective interventions. Future research should continue to explore how these oscillatory patterns interact with motor learning processes, emphasizing individualized aged-related approaches tailored to neural profiles and specific cognitive-motor needs.

The accumulated evidence underscores the complexity and variability of neural mechanisms involved in motor learning. Studies like Wu et al. (2014) and Penalver-Andres et al. (2022) further emphasize the value of examining connectivity patterns and oscillatory traits at rest to predict motor performance outcomes. The integration of EEG-based frequency analysis with behavioral and task-based metrics provides a powerful framework for understanding motor learning across the lifespan. Continued exploration of frequency-specific dynamics especially beta and its interaction with other bands may yield effective strategies for enhancing neurorehabilitation and mitigating age-related motor deficits.

From a clinical perspective, these findings underscore the value of EEG-based assessment for tailoring motor rehabilitation protocols in aging populations. For example, baseline beta power or alpha connectivity could serve as biomarkers to identify individuals who may benefit from slower-paced, distributed practice formats. Furthermore, EEG-guided neurofeedback interventions could enhance specific oscillatory patterns (e.g., increasing alpha or suppressing excessive beta), thereby improving motor learning efficiency. This neuroadaptive approach aligns well with principles of personalized medicine and could be particularly beneficial for older adults facing early-stage motor or cognitive decline.

4.1 Strengths and limitations

This systematic review is built on a comprehensive literature search conducted across multiple databases, ensuring a broad and inclusive capture of relevant studies. The predefined inclusion and exclusion criteria were carefully established to enhance the reliability and validity of the findings, allowing for a consistent evaluation of the available evidence. Quality assessment of the studies was rigorously conducted using the ROB2 (Risk of Bias in Systematic Reviews) tool, adhering to established guidelines to minimize bias and strengthen the conclusions drawn. Our review includes a detailed examination of randomized controlled trials and experimental studies that meet the inclusion criteria, ensuring a high level of evidence. Furthermore, collaboration with field experts has contributed additional insights and credibility to the review, enriching the analysis and interpretation of the data. These strengths collectively contribute to the robustness of the review, providing valuable guidance for future basic research, clinical practice in neurorehabilitation, and motor behavior studies.

Despite its strengths, this systematic review has several limitations that should be acknowledged. One of the primary concerns is the heterogeneity among the included studies, which may affect the generalizability of our findings. Variations in study design, sample sizes, assessment methods, and intervention protocols can introduce inconsistencies, making it challenging to draw definitive conclusions across diverse research contexts. Additionally, some studies included in the review had methodological flaws, such as small sample sizes, lack of proper control groups, or inadequate reporting of results, which may impact the overall quality of the evidence. Our review was also restricted to English-language publications from 2008 onwards, potentially excluding relevant studies published in other languages or prior to this period. This language and timeframe restriction may limit the comprehensiveness of our review and overlook important findings from earlier or non-English research. Moreover, while we made extensive efforts to minimize bias in study selection through rigorous screening and predefined criteria, some degree of subjectivity could still influence the interpretation and synthesis of the data. These limitations underscore the need for cautious interpretation of the results and highlight areas for improvement in future research.

In addition, many studies included in this review had relatively small sample sizes and were often underpowered to be able to detect age-related interaction effects. Furthermore, even a selected group of studies used a longitudinal design, making it difficult to draw conclusions related to long-term motor learning or retention. EEG data quality and preprocessing also varied widely, contributing to variability in reported findings. The heterogeneity of EEG feature extraction, from peak frequency to event-related desynchronization and coherence, further complicates cross-study comparison. While our selection process conformed to rigid inclusion criteria, publication bias is always a risk with the use of published data, given that studies with negative results will more likely go unreported.

Moreover, it was very common for data to not be reported or to be incomplete; for example, some studies failed to report key participant demographic (e.g., gender) or it is not possible to know how these missing key participant demographics impacted the conclusion drawn (e.g., Zhang and Fong, 2019). There were also concerns with selective reporting of outcomes (e.g., some studies reported specific outcome measures and do not report on other relevant outcomes, some outcomes that there were indications of adverse outcomes but they were not reported, etc.). This bias raises suspicion around study findings and limits robustness and generalizability study findings. Ultimately, future study should report all data so other researchers can replicate or use these findings to inform practice or implementation. There are some notable limitations in the research found during the course of this extensive systematic literature review. A number of the reviewed articles did not employ randomized controlled trial designs except for the ten studies previously identified as randomized trials and semi randomized and instead relied on experimental approaches that lacked methodological transparency with short sample sizes and short training intervention times even with representing valuable insights (e.g., Yang et al., 2017; Baumeister et al., 2013; Penalver-Andres et al., 2022).

4.1.1 Future directions

Relying on brain oscillations associated with various aspects of motor learning and performance contributes to the application of neuroscientific methods such as neurofeedback and its different types in healthy populations (Onagawa et al., 2023), athletes and highly specialized skills (Afrash et al., 2023; Xiang et al., 2018; Mirifar et al., 2017). For example, sensorimotor neurofeedback as a method used for regulating brain activities at Cz (central) area was revealed to be effective in facilitating motor learning in golfers. Moreover, it was found that neurofeedback training improved the amplitude of sensorimotor at Cz, suppressed alpha at Fz (Frontal), and is recommended to be applied in order to facilitate longer-term motor learning in golfers (Afrash et al., 2023). However, because individuals with extensive expertise often exhibit distinct neural structures and greater neuroplastic potential compared to non-experts (Furuya et al., 2014; Mizuguchi et al., 2019; Nakagawa et al., 2019), it remains uncertain whether findings observed in expert populations can be reliably applied to the general healthy population. The specialized oscillation-based training and experience of experts may lead to unique adaptations in brain function and anatomy, which could influence how they respond to experimental tasks or interventions. As a result, caution is warranted when attempting to generalize results from expert samples to broader, non-expert groups.

Future work should explore real-time EEG-based neurofeedback protocols tailored to enhance the oscillatory patterns most conducive to learning (e.g., boosting frontoparietal alpha or suppressing beta during specific learning phases). The development and validation of mobile, dry-sensor EEG systems open avenues for monitoring brain activity in everyday environments. This would allow for ecologically valid training and assessment, especially important for older adults in home or community settings. Additionally, machine learning models leveraging EEG coherence and power spectra (e.g., via partial least squares regression) could stratify individuals based on predicted learning potential, guiding the selection of interventions that are most likely to succeed. Longitudinal studies incorporating EEG, behavioral, and structural imaging data are needed to track how neural plasticity evolves with age and intervention. This would help disentangle compensatory vs. restorative mechanisms and inform maintenance strategies for cognitive-motor health. Given the demonstrated benefits of dual-task training in older adults, rehabilitation programs should incorporate cognitive challenges alongside motor tasks to enhance generalization and functional transfer, especially for populations at risk of falls or cognitive decline. We further suggest future studies to consider neurofeedback training for motor learning in healthy adults while taking the duration, protocol, number of sessions, demographics, psychological states, potential psychiatric and neurological symptoms, motor performance, and motor functions (e.g., speed, accuracy, power, and dexterity).

As also reviewed by Peng et al. (2024), a promising avenue for upcoming research involves exploring corticomuscular coherence as it relates to sensorimotor learning. In our systematic review, we learned that several studies have pointed to the importance of beta-band oscillations in executing movement, acquiring motor skills, and in the interaction between brain and muscle activity. Notably, beta corticomuscular coherence appears to play a key role in stabilizing muscle force during static (isometric) contractions. However, its relationship with variables such as movement speed and precision remains less clearly defined and deserves closer investigation. Progress in this area may come from analyzing how different brain regions coordinate with muscle groups during diverse motor tasks (Peng et al., 2024). By combining analyses of both corticomuscular and intermuscular coherence, future researchers can better understand the broader neural systems that support motor adaptation and control, such as tool use training, which was measured in our prior research (Jahanian Najafabadi et al., 2023a,b, 2025). Insights from this line of research may contribute to the development of more precise and effective therapeutic interventions for motor-related disorders, such as stroke. In addition, we propose the use of augmented and extended reality technologies as valuable tools to examine how brain activity varies in response to different task demands and environmental conditions that differ from real-world settings (Jahanian et al., 2023c). For example, virtual objects may lack physical weight, or their perceived weight can be manipulated using physical proxies, offering novel ways to study motor control and sensorimotor adaptation under controlled yet ecologically valid conditions.

5 Summary and conclusion

In summary, aging influences the neurophysiological patterns of motor learning but does not eliminate the brain's capacity for functional reorganization and skill acquisition. EEG biomarkers particularly in the alpha, theta, and beta bands along with measures of brain connectivity, offer valuable insight into the neural adaptations that occur with practice and aging. Optimizing training approaches (e.g., exergaming, distributed practice, and task difficulty calibration) can unlock latent plasticity in older adults, enabling meaningful gains in both cognitive and motor domains. These findings support a precision-based approach to rehabilitation and lifelong learning, leveraging individualized neural profiles to enhance outcomes across the lifespan.

Our findings indicate that task design is critical: interventions that are multimodal (e.g., exergaming), adaptive, and cognitively engaging produce more robust neural and behavioral changes than static or less interactive approaches. Additionally, practice structure (massed vs. distributed) and task difficulty play pivotal roles in shaping neural efficiency and long-term retention. For older adults, moderate challenge levels and spaced practice sessions appear to offer the best balance between cognitive demand and plastic potential. Moreover, task familiarity and skill consolidation processes differ with age. Older adults show slower neural adaptation and less efficient modulation of oscillatory activity, yet benefit from overnight consolidation and show sustained gains when conditions are optimal. These insights suggest that while the rate and pattern of learning may differ across the lifespan, the capacity for learning remains viable.

From a translational perspective, these findings advocate for precision-based motor training that is informed by individual neurophysiological profiles. By leveraging resting-state EEG and tracking spectral power, coherence, and real-time connectivity patterns over time, clinicians and researchers can better predict which individuals are likely to benefit from specific types of motor training and adjust protocols accordingly. In conclusion, aging does not represent a fixed barrier to motor learning. Rather, it invites a more refined, evidence-based approach that aligns task demands with the learner's cognitive and neural profile. With the integration of EEG biomarkers and personalized training frameworks, we can move toward a new era of adaptive individualized neurorehabilitation, one that promotes resilience, autonomy, and cognitive vitality well into later life.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

AJ-N: Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. ED: Data curation, Investigation, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. Bielefeld University paid full publication fees.

Acknowledgments

The authors would like to thank Dr. Carolyne Kroger for her valuable feedback and constructive input on the early version of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Gen AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abbreviations

EEG, Electroencephalography; EEG-EMG, Electroencephalography and Electromyography: A well-known quantitative techniques used for gathering biological signals at cortical and muscular levels; DMN, Default Mode Network; TEP, transcranial magnetic stimulation-evoked potential; DCM, Dynamic causal modeling; PSD, Power Spectral Density; rsFC, resting-state functional connectivity; FC, functional connectivity; ERD, event-related desynchronization; ERS, event-related synchronization; PS, phase synchronization; FFT, the Fast Fourier Transform; MRBD, motor-related beta-dynamics; PMBR, post-movement beta rebound; ROIs, Regions of interest; RT, reaction times; MVF, mirror visual feedback; GBA, Gamma-Band Activity; Iγ, The gamma index; M1, The primary motor cortex; SMA, The supplementary motor area; PMv, ventral premotor cortex; PMd, the dorsal premotor cortex; S1, the primary somatosensory cortex; PPC, the posterior parietal cortex; MFC, medial frontal cortex; PFC, prefrontal cortex.

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