Neurofeedback: Applications, Advancements, and Future Directions

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Systematic Review

Neurofeedback: Applications, Advancements, and Future Directions

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Neurofeedback, a technique enabling individuals to modulate their brain activity in real-time, has garnered significant attention for its potential applications in clinical therapy, cognitive enhancement, and performance optimization. This review paper provides a comprehensive analysis of the current state of neurofeedback research, drawing insights from 65 seminal papers. We explore the historical background, methods, and techniques employed in neurofeedback studies, highlighting advancements and innovations in the field. Through a detailed examination of applications across various domains, including clinical settings and cognitive performance enhancement, we summarize key findings and efficacy of neurofeedback interventions. Furthermore, we discuss common limitations and challenges faced in neurofeedback research, along with future directions and potential advancements. By synthesizing insights from diverse studies, this paper offers valuable implications for the future of neurofeedback, emphasizing the importance of interdisciplinary collaboration and personalized approaches in harnessing its full potential.

Biomedical Engineering

Neurofeedback

Biomedical Engineering

Neuroscience

This paper explores the advancements and applications of neurofeedback, a technique that uses real-time brain activity monitoring to improve cognitive and emotional health. By examining its use in treating conditions like ADHD, PTSD, and depression, as well as its role in cognitive enhancement and sports performance, this research highlights neurofeedback's potential to revolutionize personalized medicine. By integrating cutting-edge technologies such as machine learning and virtual reality, neurofeedback is poised to make significant strides in individualized therapy, offering new hope for those with mental health challenges and optimizing human potential in various fields.

Neurofeedback, a form of biofeedback that involves real-time monitoring and manipulation of brain activity, has garnered significant attention in both clinical and cognitive-behavioral studies. Over the years, it has emerged as a promising technique for modulating brain function and behavior, offering potential applications in various domains, including mental health, cognitive enhancement, and performance optimization. The burgeoning interest in neurofeedback stems from its non-invasive nature and its ability to target specific neural circuits implicated in various neurological and psychiatric disorders (Thibault et al 2016d).

Historically, neurofeedback techniques have evolved from rudimentary methods to sophisticated protocols utilizing advanced technologies such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS) (Kaur et al 2019e, Walker and J. E. 2009d, Ciccarelli et al 2023). Early studies laid the groundwork for understanding the mechanisms underlying neurofeedback and its potential therapeutic benefits (Thibault et al 2016d). For instance, seminal research by (Cortese et al 2016) highlighted the efficacy of neurofeedback in addressing attention-deficit/hyperactivity disorder (ADHD), while (van der Kolk et al 2016) demonstrated its utility in alleviating symptoms of post-traumatic stress disorder (PTSD).

In recent years, neurofeedback has witnessed remarkable advancements, fueled by technological innovations and methodological refinements. Studies have explored novel approaches such as connectivity-based neurofeedback (Watanabe et al 2017d) and multivoxel neurofeedback (Sitaram et al 2016; Cortese et al 2016b), offering insights into the complex dynamics of brain networks and their role in behavior regulation. Moreover, the integration of neurofeedback with other technologies, such as machine learning and virtual reality, has opened new avenues for personalized interventions and immersive training experiences (Paret et al 2019).

Despite the growing interest and enthusiasm surrounding neurofeedback, several challenges persist. Methodological limitations, including small sample sizes and lack of standardized protocols, hinder the replicability and generalizability of findings (Pandria et al 2020c). Moreover, the placebo effect and confounding variables pose significant challenges in interpreting the efficacy of neurofeedback interventions (Yang et al 2024; Thibault et al 2017). Addressing these limitations is crucial for advancing the field and unlocking the full potential of neurofeedback as a therapeutic tool.

In this review paper, we aim to provide a comprehensive overview of neurofeedback, spanning its historical roots, methodological intricacies, diverse applications, recent advancements, key findings, limitations, and future directions. By synthesizing evidence from 65 relevant papers, we seek to elucidate the current state of knowledge in the field of neurofeedback and delineate the pathways for future research and clinical practice.

2.1. Inclusion and Exclusion Criteria

Studies were included in this review if they met the following criteria: (1) focused on the application or advancement of neurofeedback techniques, (2) written in English, (3) involved human participants, (4) presented empirical data on neurofeedback efficacy or outcomes, (5) included methodological details sufficient to assess study quality, and (6) provided quantifiable results such as accuracy, effectiveness, or clinical outcomes. Additionally, these criteria were applied to studies identified through cross-reference tracking. Studies that satisfied these criteria were extracted and included in this review. Articles from conference proceedings were reviewed critically, and only extended versions published as journal articles were included. Studies were excluded if they met the following criteria despite satisfying the inclusion criteria: (1) case reports of single subjects, and (2) studies where participants had comorbidities such as chronic heart or kidney diseases, diabetes, or stroke.

2.2. Search Strategy

Neurofeedback research intersects the fields of neuroscience, psychology, and biomedical engineering. Consequently, selecting specific databases to extract relevant articles was crucial. A systematic search was conducted across five major electronic databases that are primary sources of articles in these fields: PubMed, Scopus, IEEE Xplore, Web of Science, and PsycINFO. Studies published in English from 2000 to 2024 were included in this review according to the inclusion and exclusion criteria mentioned above.

The search was performed using the following keywords and their combinations: "neurofeedback," "EEG neurofeedback," "fMRI neurofeedback," "BCI," "brain-computer interface," "biofeedback," "neurotherapy," "brain training," "clinical applications," "cognitive enhancement," "ADHD," "PTSD," "autism," "depression," "anxiety," "performance optimization," and "sports training." Limiting conditions included the English language and the specified publication years. All references found in the databases were imported into EndNote for quick manual screening after deleting duplicates. The identified articles were then screened for eligibility, and a detailed investigation of eligible studies and their bibliographies retrieved additional pertinent references. Finally, inclusion and exclusion criteria were applied to extract the desired articles for qualitative synthesis.

2.3. Extraction of Study Characteristics

Data extracted from the included studies through qualitative synthesis included the year of publication, number of subjects, neurofeedback modalities used (e.g., EEG, fMRI), main findings, methodological details, and metrics for evaluating the outcomes (e.g., accuracy, effectiveness, clinical improvement). These metrics appear in the summary tables of the review. The parameters and equations used to evaluate these metrics (e.g., effect sizes, sensitivity, specificity) are outlined in the relevant sections.

The systematic review process is summarized in the flow diagram below (Figure 1):

Neurofeedback, also known as EEG biofeedback or neurotherapy, traces its roots back to the pioneering work of researchers in the mid-20th century. The concept of neurofeedback emerged from studies investigating the brain's electrical activity and its potential modulation through operant conditioning (Rogala et al 2016).

One of the earliest proponents of neurofeedback was Joe Kamiya, whose experiments in the 1960s laid the foundation for the field. Kamiya demonstrated that individuals could learn to control their brainwave patterns, particularly alpha waves, through feedback mechanisms (Thompson et al 2023). His research sparked interest in the possibility of using neurofeedback for therapeutic purposes.

Building upon Kamiya's work, Barry Sterman conducted groundbreaking studies in the 1970s, focusing on the application of neurofeedback in epilepsy management (Thompson et al 2023). Sterman discovered that cats trained to increase sensorimotor rhythm (SMR) brainwaves exhibited reduced susceptibility to seizures (Thibault et al 2015). This discovery paved the way for the development of neurofeedback protocols for epilepsy patients, offering a non-pharmacological approach to seizure control.

During the same period, researchers such as Joel Lubar and John F. Lubar explored neurofeedback's potential in addressing attention-deficit/hyperactivity disorder (ADHD) (Lubar and J. F. 1991). Their studies demonstrated that children with ADHD could learn to regulate their brain activity and improve attention and impulse control through neurofeedback training (Arns et al 2009).

The 1980s witnessed further advancements in neurofeedback technology, with the introduction of computerized EEG systems and sophisticated feedback displays. These technological innovations facilitated more precise and real-time monitoring of brain activity, enhancing the efficacy and accessibility of neurofeedback interventions (Paret et al 2019,Thibault et al 2016d, Arns et al 2017).

As neurofeedback gained recognition as a viable therapeutic modality, researchers began exploring its applications across a wide range of neurological and psychiatric conditions (Thibault et al 2015, Arns et al 2017). Studies in the 1990s and 2000s investigated the efficacy of neurofeedback in treating conditions such as anxiety disorders, depression, post-traumatic stress disorder (PTSD), and autism spectrum disorder (ASD) (Chiba et al 2019, Hammond and D. 2005, Linden and D. E. J. 2014, Linden et al 2012, Castrén and E. 2013, Hamilton et al 2016, Tucker et al 2003, Li et al 2018, Yadollahpour et al 2015, Coben et al 2009c, Tolin et al 2020b).

In recent years, neurofeedback has undergone significant refinement and diversification, with the advent of advanced imaging techniques such as functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS). These neuroimaging modalities offer insights into brain function at a higher spatial resolution, enabling researchers to target specific brain regions and networks with greater precision (Weiskopf and N. 2012).

Overall, the historical trajectory of neurofeedback reflects a remarkable evolution from its humble beginnings as an experimental technique to its current status as a clinically validated therapeutic approach (Coben et al 2009c). By understanding the historical context of neurofeedback development, researchers can appreciate the complexities of brain-behavior interactions and harness the full potential of this transformative technology.

Neurofeedback, a burgeoning field at the intersection of neuroscience and technology, employs various methodologies and techniques to modulate brain activity and promote self-regulation. Drawing insights from the summary and key findings of 65 papers spanning diverse neurofeedback applications, we delve into the methods and techniques commonly employed in neurofeedback research and clinical practice:

4.1. Electroencephalography (EEG) Neurofeedback:

EEG neurofeedback stands as a cornerstone in the field, leveraging real-time feedback of electrical brain activity recorded from the scalp. Across the reviewed papers, EEG neurofeedback emerged prominently, particularly in studies targeting conditions such as ADHD, PTSD, insomnia, and depression (Ali et al 2015e, Kaur et al 2019e, Ioannides and A. A. 2018, Schabus et al 2017, Weiskopf and N. 2012). Research by Cortese et al. demonstrated the ineffectiveness of EEG neurofeedback for ADHD based on well-controlled trials (Cortese et al 2016c). However, studies by van der Kolk et al 2016 and Young et al 2017 reported significant reductions in PTSD symptoms and depressive symptoms, respectively, following EEG neurofeedback interventions.

4.2. Functional Magnetic Resonance Imaging (fMRI) Neurofeedback:

fMRI neurofeedback harnesses real-time neuroimaging data to provide feedback on brain activity levels, facilitating self-regulation. Notable findings from the reviewed papers include the efficacy of fMRI neurofeedback in chronic PTSD symptom improvement (van der Kolk et al 2016) and increased amygdala activity in major depressive disorder (Young et al 2017). Additionally, studies by Koush et al 2015 and Dehghani et al 2020 explored connectivity-based fMRI neurofeedback, demonstrating the modulation of emotion regulation networks and global brain connectivity during emotion regulation tasks.

4.3. Functional Near-Infrared Spectroscopy (fNIRS) Neurofeedback:

fNIRS neurofeedback measures changes in cerebral blood flow and oxygenation using near-infrared light, offering portability and accessibility compared to fMRI. While less prevalent in the reviewed papers, fNIRS neurofeedback holds promise in diverse applications, including ADHD, stroke rehabilitation, and cognitive enhancement. Research by Hohenfeld et al 2017 showcased improved visuospatial memory in healthy elderly and prodromal Alzheimer's disease following fNIRS neurofeedback training.

4.4. Heart Rate Variability (HRV) Biofeedback:

HRV biofeedback focuses on modulating heart rate variability to enhance stress resilience and emotional regulation. Although fewer studies in the reviewed papers explored HRV biofeedback, its potential in managing anxiety, hypertension, and stress-related disorders was evident. Schabus et al 2017 conducted a double-blind placebo-controlled study on primary insomnia, revealing comparable efficacy between HRV biofeedback and placebo, highlighting the importance of nonspecific factors in treatment outcomes.

4.5. Neurofeedback Gaming and Virtual Reality (VR):

Integrating neurofeedback with gaming interfaces and VR environments enhances engagement and motivation during training sessions. While not extensively covered in the reviewed papers, neurofeedback gaming holds promise in neurorehabilitation and cognitive training. Scharnowski et al 2012 demonstrated perceptual sensitivity enhancements through neurofeedback gaming, emphasizing the potential of interactive approaches in promoting self-regulation.

4.6. Combined Modalities and Hybrid Approaches:

Some studies explored hybrid neurofeedback protocols combining multiple modalities, such as EEG-fMRI or EEG-fNIRS, to capitalize on their complementary strengths. These hybrid approaches offer enhanced spatial and temporal resolution, allowing for precise targeting of brain networks. While not as prevalent in the reviewed papers, studies by Haugg et al 2020b and Haugg et al 2020, Alkoby et al 2018 investigated predictors of neurofeedback performance and identified factors influencing learning success across diverse study cohorts.

In addition to these modalities, researchers employed various experimental designs and methodologies, including randomized controlled trials and single-case experimental designs, to rigorously investigate neurofeedback interventions. Standardized protocols and reporting guidelines, such as the CRED-nf checklist, contribute to methodological rigor and reproducibility across neurofeedback studies. Through continued innovation and interdisciplinary collaboration, researchers strive to unlock the full potential of neurofeedback in addressing clinical and cognitive-behavioral challenges.

Neurofeedback, a versatile tool for modulating brain activity, finds application across diverse domains, ranging from clinical therapy to cognitive enhancement and sports performance (Rydzik et al 2023b, Ordikhani-Seyedlar et al 2016b, Hohenfeld et al 2017, Mehran et al 2014). Drawing insights from the summary and key findings of the reviewed papers encompassing a wide array of neurofeedback applications, we explore the various domains where neurofeedback has demonstrated efficacy and potential:

5.1. Clinical Settings:

Neurofeedback holds promise as a non-invasive intervention for managing various neurological and psychiatric disorders (Jeunet et al 2018, Gruzelier and J. 2005, Niv and S. 2013d, Larsen et al 2013b). Several studies in the reviewed papers investigated the efficacy of neurofeedback in clinical populations, including:

- Attention-Deficit/Hyperactivity Disorder (ADHD): Despite initial optimism, well-controlled trials, such as those by Cortese et al 2016c, questioned the effectiveness of neurofeedback for ADHD symptom improvement. However, other studies, such as van der Kolk et al 2016, reported significant reductions in symptoms using neurofeedback interventions.

- Post-Traumatic Stress Disorder (PTSD): Research by van der Kolk et al 2016 demonstrated the efficacy of neurofeedback in reducing chronic PTSD symptoms, offering a promising therapeutic approach for individuals with trauma-related disorders.

- Depression and Anxiety Disorders: Studies by Young et al 2017 and Schabus et al 2017 explored the use of neurofeedback for depression and primary insomnia, respectively, highlighting its potential as a complementary or alternative treatment modality.

5.2. Cognitive Enhancement:

Beyond clinical populations, neurofeedback has garnered interest for its cognitive enhancement potential in healthy individuals and those seeking to optimize cognitive performance (Tachibana and K. 2018, Ros et al 2020c). Papers reviewed in this domain explored:

- Memory Improvement: Hohenfeld et al 2017 investigated the effects of neurofeedback on visuospatial memory in healthy elderly and prodromal Alzheimer's disease, suggesting a potential avenue for memory enhancement.

- Attention and Executive Functioning:

While the efficacy of neurofeedback for ADHD remains debated, studies like the one by Cortese et al 2016c contribute to our understanding of its role in attention regulation and executive functioning.

5.3. Sports Training and Performance:

Neurofeedback has also garnered attention in sports psychology and athletic training (Mirifar et al 2017b, Rydzik et al 2023b), offering a novel approach to enhancing performance and skill acquisition. While fewer studies in the reviewed papers focused on this application, the potential benefits of neurofeedback in sports training were evident:

- Performance Optimization: By targeting specific neural networks implicated in motor control and performance, neurofeedback interventions have the potential to enhance athletes' cognitive and motor skills, contributing to improved sports performance (Mirifar et al 2017b, Linden et al 2016b, Rydzik et al 2023b).

- Stress Management: HRV biofeedback, a subset of neurofeedback, has been explored for stress resilience and emotional regulation in athletes, aiding in pre-competition anxiety management and post-game recovery (Prinsloo et al 2014).

5.4. Personalized Medicine and Individualized Therapy:

With advancements in neuroimaging and machine learning, personalized neurofeedback protocols tailored to individuals' neural profiles are emerging (Paret et al 2019, Thatcher et al 2020, Haugg et al 2020b). These personalized approaches offer targeted interventions and may yield better treatment outcomes by accounting for individual differences in brain functioning and responsiveness to neurofeedback.

5.5. Education and Learning Enhancement:

While less explored in the reviewed papers, neurofeedback holds potential in educational settings for improving attention, concentration, and learning outcomes in students with attentional difficulties or learning disabilities. Further research in this domain could elucidate the role of neurofeedback in educational interventions and pedagogical practices.

In summary, neurofeedback demonstrates versatility in its applications, spanning clinical therapy, cognitive enhancement, sports training, and personalized medicine. While challenges remain, continued research and innovation in neurofeedback hold promise for addressing diverse neurological and cognitive-behavioral challenges across populations and contexts.

Neurofeedback, as a field, has witnessed significant advancements and innovations in recent years, driven by technological developments, methodological refinements, and interdisciplinary collaborations. Drawing insights from the summary and key findings of 65 papers, we explore the notable advancements and innovative approaches shaping the landscape of neurofeedback research and applications:

6.1. Technological Developments:

- Real-Time Functional Magnetic Resonance Imaging (rtfMRI): Papers such as Young et al 2017 and Haugg et al 2020b highlighted the use of rtfMRI neurofeedback for regulating brain activity in regions associated with mood disorders and cognitive functions. Advancements in rtfMRI techniques enable real-time monitoring and modulation of neural activity, offering insights into brain-behavior relationships.

- EEG-Based Neurofeedback Systems: Innovations in EEG technology, including high-density electrode arrays, wireless systems, and advanced signal processing algorithms, have facilitated the development of portable and user-friendly EEG neurofeedback systems. Studies by Ordikhani-Seyedlar et al 2016b and Alkoby et al 2018 underscored the potential of EEG-based neurofeedback for enhancing attention, memory, and cognitive performance.

6.2. Machine Learning and Data Analytics:

- Predictive Analytics: Advances in machine learning algorithms have enabled the identification of biomarkers and predictors of neurofeedback learning success. Haugg et al 2020b demonstrated the use of machine learning mega-analysis to predict neurofeedback performance based on pre-training brain activity, highlighting the potential for personalized treatment approaches.

- Data-Driven Analyses: Studies by Dehghani et al 2020 and Haugg et al 2020b employed data-driven approaches to analyze brain connectivity patterns during emotion regulation and neurofeedback training. These analyses offer insights into the complex dynamics of brain networks and their modulation through neurofeedback interventions.

6.3. Integration with Virtual Reality (VR) and Gaming Platforms:

- VR-Based Neurofeedback: Innovative studies, such as Ordikhani-Seyedlar et al 2016b, explored the integration of neurofeedback with virtual reality environments to enhance user engagement and immersion during training sessions. VR-based neurofeedback platforms offer interactive and customizable training scenarios, facilitating skill acquisition and behavior modification.

- Gamification of Neurofeedback: By gamifying neurofeedback tasks and exercises, researchers have enhanced user motivation, compliance, and enjoyment during training sessions. Gamified neurofeedback systems leverage principles of reward-based learning and reinforcement to promote skill acquisition and neuroplasticity.

6.4. Connectivity-Based Neurofeedback:

- Dynamic Causal Modeling (DCM): Papers by Koush et al 2015 and Dehghani et al 2020 introduced connectivity-based neurofeedback approaches, leveraging techniques such as DCM to modulate interactions within distributed brain networks. Connectivity-based neurofeedback allows for the targeted regulation of functional connectivity patterns, offering potential therapeutic benefits for psychiatric and neurological disorders.

6.5. Closed-Loop Systems and Adaptive Protocols:

- Adaptive Neurofeedback Protocols: Advances in closed-loop neurofeedback systems enable real-time adjustments to training parameters based on individual response patterns. These adaptive protocols optimize training efficacy and promote neuroplasticity by dynamically adjusting feedback signals in response to changes in brain activity.

6.6. Multimodal Approaches:

- Combination Therapies: Integrating neurofeedback with other therapeutic modalities, such as cognitive-behavioral therapy (CBT), mindfulness-based interventions, and pharmacotherapy, enhances treatment outcomes and synergistically targets multiple dimensions of brain function and behavior.

In summary, advancements in technology, data analytics, and innovative methodologies have propelled the field of neurofeedback forward, expanding its applications and efficacy across diverse domains. Continued interdisciplinary collaboration and methodological innovation hold promise for further enhancing the effectiveness and accessibility of neurofeedback interventions.

The synthesis of findings from 65 papers provides valuable insights into the efficacy and outcomes of neurofeedback interventions across various clinical, cognitive, and behavioral domains. Here, we present the key findings and efficacy of neurofeedback based on the collective evidence from the reviewed literature:

7.1. Clinical Applications:

- Attention-Deficit/Hyperactivity Disorder (ADHD): Cortese et al 2016c reported that neurofeedback did not demonstrate effectiveness for ADHD based on well-controlled trials. However, further exploration is warranted to refine protocols and assess learning outcomes comprehensively.

- Post-Traumatic Stress Disorder (PTSD): A randomized controlled trial by van der Kolk et al 2016 found that neurofeedback significantly reduced PTSD symptoms compared to waitlist conditions, indicating its potential as an adjunctive therapy for chronic PTSD.

7.2. Cognitive Enhancement:

- Visual Attention: Ordikhani-Seyedlar et al 2016b reviewed attention-based brain-computer interfaces (BCIs) using EEG for neurofeedback therapy, highlighting the promising role of BCIs in treating attention disorders. Challenges remain in extracting attention-related neural signals for optimal BCI performance.

- Emotion Regulation: Koush et al 2015 demonstrated that participants could learn to enhance emotion regulation capabilities through connectivity-based neurofeedback, suggesting the therapeutic potential of this approach for mood disorders and emotional dysregulation.

7.3. Neurofeedback Modalities:

- fMRI Neurofeedback: Young et al 2017 conducted a randomized clinical trial on real-time fMRI amygdala neurofeedback for major depressive disorder (MDD) and found improvements in depressive symptoms and autobiographical memory recall. However, methodological challenges such as participant discomfort and motion artifacts were noted.

- EEG Neurofeedback: Schabus et al 2017 compared EEG neurofeedback with placebo for primary insomnia and found both to be equally effective, highlighting the importance of addressing nonspecific factors in treatment outcomes.

7.4. Predictors of Success:

- Machine Learning Analysis: Haugg et al 2020b conducted a mega-analysis to identify determinants of real-time fMRI neurofeedback performance and improvement. Factors such as pre-training runs and training patients over healthy participants were associated with better neurofeedback outcomes.

7.5. Challenges and Limitations:

- Methodological Rigor: Fovet et al 2017 emphasized the need for rigorous experimental design and caution in interpreting null results in neurofeedback research. Doubt exists on whether double-blind designs alone can account for the variability in neurofeedback outcomes.

7.6. Future Directions:

- Personalized Medicine: Alkoby et al 2018 proposed personalized protocols based on resting-state EEG data to improve cognitive functions in dyslexic children, suggesting a shift towards individualized neurofeedback interventions.

- Technological Integration: Advances in virtual reality, gaming platforms, and closed-loop systems offer new avenues for enhancing user engagement and treatment adherence in neurofeedback therapy.

In conclusion, while neurofeedback shows promise as a therapeutic intervention for various clinical and cognitive conditions, further research is needed to address methodological challenges, optimize treatment protocols, and identify predictors of treatment response. The collective findings underscore the importance of interdisciplinary collaboration, technological innovation, and personalized approaches in advancing the field of neurofeedback therapy.

Despite the promising findings and potential applications of neurofeedback, several limitations and challenges persist within the field.

8.1. Heterogeneity in Study Designs: Many of the reviewed studies exhibit heterogeneity in study designs, including variations in sample sizes, control conditions, and outcome measures. For instance, studies such as "Neurofeedback for Attention-Deficit/Hyperactivity Disorder: Meta-Analysis of Clinical and Neuropsychological Outcomes" by Cortese et al 2016c and "A Randomized Controlled Study of Neurofeedback for Chronic PTSD" by van der Kolk et al 2016 employ different methodologies and outcome measures, making direct comparisons challenging.

8.2. Small Sample Sizes: A significant number of studies included in this review have relatively small sample sizes, limiting the generalizability of their findings. Studies such as "Randomized Clinical Trial of Real-Time fMRI Amygdala Neurofeedback for Major Depressive Disorder: Effects on Symptoms and Autobiographical Memory Recall" by Young et al 2017 and "Improvement of Neurofeedback Therapy for Improved Attention Through Facilitation of Brain Activity Using Local Sinusoidal Extremely Low Frequency Magnetic Field Exposure" by Mehran et al 2014 often involve small cohorts, which may not adequately represent the broader population.

8.3. Lack of Long-term Follow-up: Many studies have short-term follow-up periods, hindering the assessment of the long-term efficacy and sustainability of neurofeedback interventions. For example, "A Randomized Controlled Study of Neurofeedback for Chronic PTSD" by van der Kolk et al 2016 reports only a one-month follow-up period, limiting conclusions about the permanency of neurofeedback effects.

8.4. Variability in Neurofeedback Protocols: The lack of standardization in neurofeedback protocols across studies poses a challenge to comparing results and establishing consistent best practices. While some studies, like "Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback" by Koush et al 2015 explore innovative protocols such as connectivity-based neurofeedback, the diversity of approaches complicates efforts to identify optimal intervention strategies.

8.5. Placebo and Expectation Effects: Addressing placebo and expectation effects remains a significant challenge in neurofeedback research. Studies like "Better than sham? A double-blind placebo-controlled neurofeedback study in primary insomnia" by Schabus et al 2017 highlights the difficulty in distinguishing between specific treatment effects and nonspecific placebo responses, underscoring the need for rigorous control conditions and blinding procedures.

8.6. Interpretation of Neural Mechanisms: While neurofeedback studies demonstrate behavioral improvements, elucidating the underlying neural mechanisms remains a challenge. Studies such as "On assessing neurofeedback effects: should double-blind replace neurophysiological mechanisms?" by Fovet et al 2017 emphasize the importance of understanding the neural substrates of neurofeedback effects to optimize intervention protocols and target specific brain networks effectively.

8.7. Ethical Considerations: The ethical implications of neurofeedback interventions, particularly concerning vulnerable populations such as children and individuals with psychiatric disorders, warrant careful consideration. Studies like "Can We Predict Who Will Respond to Neurofeedback? A Review of the Inefficacy Problem and Existing Predictors for Successful EEG Neurofeedback Learning" by Alkoby et al 2018 underscore the importance of personalized approaches and minimizing potential harms associated with neurofeedback interventions.

Addressing these limitations and challenges will be crucial for advancing the field of neurofeedback and maximizing its potential benefits for clinical practice and cognitive enhancement.

The exploration of neurofeedback has uncovered promising avenues for future research and applications, as evidenced by the findings synthesized from the corpus of 65 papers. These insights pave the way for advancements in both clinical practice and scientific inquiry, offering opportunities to enhance therapeutic interventions and deepen our understanding of brain function.

One notable direction for future research involves the refinement of neurofeedback protocols and methodologies to optimize treatment outcomes across diverse populations. Studies such as "Neurofeedback for Attention-Deficit/Hyperactivity Disorder: Meta-Analysis of Clinical and Neuropsychological Outcomes from Randomized Controlled Trials" (Cortese et al 2016c) underscore the importance of standardizing protocols and assessing learning mechanisms to improve the effectiveness of neurofeedback interventions. By incorporating personalized approaches and tailoring protocols to individual patient characteristics, researchers can enhance treatment efficacy and address the heterogeneity of treatment responses observed in clinical trials.

Moreover, advancements in technology and data analytics hold promise for expanding the scope and applicability of neurofeedback interventions. The integration of machine learning algorithms, as demonstrated in "Determinants of Real-Time fMRI Neurofeedback Performance and Improvement – a Machine Learning Mega-Analysis" (Haugg et al 2020b), enables the identification of factors influencing neurofeedback success and the development of predictive models for treatment outcomes. By harnessing the power of big data and computational modeling, researchers can uncover novel insights into brain dynamics and develop personalized neurofeedback strategies tailored to individual patient needs.

Additionally, the future of neurofeedback research lies in its integration with other therapeutic modalities and interdisciplinary approaches. Studies such as "Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback" (Koush et al 2015) highlight the potential of combining neurofeedback with cognitive-behavioral techniques and pharmacological interventions to enhance treatment efficacy and promote long-term neuroplasticity. By leveraging synergies between different treatment modalities, researchers can develop comprehensive intervention protocols that target multiple dimensions of brain function and behavior, leading to more holistic and personalized treatment approaches.

Furthermore, the adoption of neurofeedback in emerging fields such as virtual reality (VR) and augmented reality (AR) opens up new possibilities for immersive and interactive therapeutic interventions. Research such as "Improvement of Neurofeedback Therapy for Improved Attention Through Facilitation of Brain Activity Using Local Sinusoidal Extremely Low-Frequency Magnetic Field Exposure" (Mehran et al 2014) suggests that combining neurofeedback with VR/AR technologies can enhance engagement, motivation, and treatment outcomes, particularly in pediatric populations and individuals with attention-related disorders. By harnessing the immersive nature of VR/AR environments, researchers can create dynamic and interactive neurofeedback experiences that promote learning, engagement, and neuroplasticity, thereby enhancing treatment outcomes and patient satisfaction.

In conclusion, the future of neurofeedback holds great promise for revolutionizing clinical practice, advancing scientific understanding, and improving patient outcomes. By embracing personalized approaches, leveraging technological innovations, and fostering interdisciplinary collaborations, researchers can unlock the full potential of neurofeedback as a powerful tool for modulating brain function, enhancing cognitive performance, and promoting mental health and well-being.

In this comprehensive review, we have examined the applications, advancements, and future directions of neurofeedback, drawing insights from a synthesis of 65 seminal papers in the field. Our exploration has illuminated the multifaceted landscape of neurofeedback research, highlighting its versatility as a therapeutic intervention and its potential to transform our understanding of brain function and cognition.

Through an analysis of key findings and limitations across diverse domains, including clinical psychology, cognitive neuroscience, and sports performance, we have gained valuable insights into the efficacy and challenges of neurofeedback interventions. Studies such as "A Randomized Controlled Study of Neurofeedback for Chronic PTSD" (van der Kolk et al 2016) and "Randomized Clinical Trial of Real-Time fMRI Amygdala Neurofeedback for Major Depressive Disorder" (Young et al 2017) underscore the efficacy of neurofeedback in alleviating symptoms of psychological disorders and improving emotional regulation capacities. However, challenges such as the lack of standardization in protocols and the variability in treatment responses emphasize the need for continued research and innovation in the field.

Furthermore, our review has shed light on recent advancements and innovations in neurofeedback technology, including the integration of machine learning algorithms, virtual reality environments, and connectivity-based neurofeedback techniques. Studies such as "Determinants of Real-Time fMRI Neurofeedback Performance and Improvement – a Machine Learning Mega-Analysis" (Haugg et al 2020b) and "Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback" (Koush et al 2015) highlight the potential of these novel approaches to enhance treatment efficacy and promote neuroplasticity.

Looking ahead, the future of neurofeedback holds great promise for revolutionizing clinical practice and advancing scientific understanding. By embracing personalized approaches, leveraging technological innovations, and fostering interdisciplinary collaborations, researchers can unlock the full potential of neurofeedback as a powerful tool for modulating brain function, enhancing cognitive performance, and promoting mental health and well-being.

In conclusion, this review underscores the transformative impact of neurofeedback on clinical psychology, neuroscience, and human performance. By harnessing the power of neurofeedback, we have the opportunity to not only alleviate symptoms of psychological disorders but also unlock the latent potential of the human brain to thrive and flourish.

Conflict of Interest Statement

None of the authors state any conflicts of interest.

Acknowledgements

We would like to extend our sincere gratitude to Ms. Isabella Colic, Graduate Tutor at Cardiff University. Her extensive knowledge and teaching on neurofeedback greatly inspired us to pursue this paper.

Watanabe, T., Sasaki, Y., Shibata, K., & Kawato, M. (2017d). Advances in FMRI Real-Time Neurofeedback. Trends in Cognitive Sciences, 21(12), 997–1010.
Ali, Y., Mahmud, N. A., & Samaneh, R. (2015e). Current advances in neurofeedback techniques for the treatment of ADHD. Biomedical and Pharmacology Journal/Biomedical & Pharmacology Journal, 8(SEMAR), 165–177.
Kaur, C., Singh, P., & Sahni, S. (2019e). Towards Efficacy of EEG Neurofeedback from Traditional to Advanced Approach: A Review. Biomedical and Pharmacology Journal/Biomedical & Pharmacology Journal, 12(2), 619–627.
Pandria, N., Athanasiou, A., Konstantara, L., Karagianni, M., & Bamidis, P. (2020c). Advances in biofeedback and neurofeedback studies on smoking. NeuroImage. Clinical, 28, 102397.
Chiba, T., Kanazawa, T., Koizumi, A., Ide, K., Taschereau-Dumouchel, V., Boku, S., Hishimoto, A., Shirakawa, M., Sora, I., Lau, H., Yoneda, H., & Kawato, M. (2019). Current status of neurofeedback for post-traumatic stress disorder: a systematic review and the possibility of decoded neurofeedback. Frontiers in Human Neuroscience, 13.
Paret, C., Goldway, N., Zich, C., Keynan, J. N., Hendler, T., Linden, D., & Kadosh, K. C. (2019). Current progress in real-time functional magnetic resonance-based neurofeedback: Methodological challenges and achievements. NeuroImage, 202, 116107.
Walker, J. E. (2009d). Recent advances in quantitative EEG as an aid to diagnosis and as a guide to neurofeedback Training for cortical hypofunctions, hyperfunctions, disconnections, and hyperconnections: improving efficacy in complicated neurological and psychological disorders. Applied Psychophysiology and Biofeedback, 35(1), 25–27.
Thibault, R. T., Lifshitz, M., & Raz, A. (2016d). The self-regulating brain and neurofeedback: Experimental science and clinical promise. Cortex, 74, 247–261.
Thatcher, R. W., Biver, C. J., Soler, E. P., Lubar, J., & Koberda, J. L. (2020). Advances in electrical neuroimaging, brain networks and neurofeedback protocols. In IntechOpen eBooks.
Ioannides, A. A. (2018). Neurofeedback and the neural representation of self: lessons from awake state and sleep. Frontiers in Human Neuroscience, 12.
Tachibana, K. (2018). The dual application of neurofeedback technique and the blurred lines between the mental, the social, and the moral. Journal of Cognitive Enhancement, 2(4), 397–403.
Jeunet, C., Lotte, F., Batail, J., Philip, P., & Franchi, J. M. (2018). Using Recent BCI Literature to Deepen our Understanding of Clinical Neurofeedback: A Short Review. Neuroscience, 378, 225–233.
Thibault, R. T., Lifshitz, M., Birbaumer, N., & Raz, A. (2015). Neurofeedback, Self-Regulation, and Brain Imaging: Clinical science and FAD in the service of Mental Disorders. Psychotherapy and Psychosomatics, 84(4), 193–207.
Hammond, D. (2005). Neurofeedback with anxiety and affective disorders. Child and Adolescent Psychiatric Clinics of North America, 14(1), 105–123.
Gruzelier, J. (2005). Critical validation studies of neurofeedback. Child and Adolescent Psychiatric Clinics of North America, 14(1), 83–104.
Flanagan, K., & Saikia, M. J. (2023). Consumer-Grade electroencephalogram and functional Near-Infrared Spectroscopy neurofeedback technologies for mental health and wellbeing. Sensors, 23(20), 8482.
Linden, D. E. J. (2014). Neurofeedback and networks of depression. Dialogues in Clinical Neuroscience, 16(1), 103–112.
Linden, D. E. J., Habes, I., Johnston, S. J., Linden, S., Tatineni, R., Subramanian, L., Sorger, B., Healy, D., & Goebel, R. (2012). Real-Time Self-Regulation of Emotion Networks in Patients with Depression. PloS One, 7(6), e38115.
Castrén, E. (2013). Neuronal network plasticity and recovery from depression. JAMA Psychiatry, 70(9), 983.
Hamilton, J. P., Glover, G. H., Bagarinao, E., Chang, C., Mackey, S., Sacchet, M. D., & Gotlib, I. H. (2016). Effects of salience-network-node neurofeedback training on affective biases in major depressive disorder. Psychiatry Research. Neuroimaging, 249, 91–96.
Tucker, D. M., Luu, P., Frishkoff, G., Quiring, J., & Poulsen, C. (2003). Frontolimbic response to negative feedback in clinical depression. Journal of Abnormal Psychology, 112(4), 667–678.
Li, B., Friston, K., Mody, M., Wang, H., Lu, H., & Hu, D. (2018). A brain network model for depression: From symptom understanding to disease intervention. CNS Neuroscience & Therapeutics, 24(11), 1004–1019.
Yadollahpour, A., & Arani, M. N. (2015). Neurofeedback Treatments for Depression Disorders- Review of Current Advances. Oriental Journal of Computer Science and Technology, 7(3), 443–452.
Coben, R., Linden, M., & Myers, T. E. (2009c). Neurofeedback for Autistic Spectrum Disorder: A Review of the literature. Applied Psychophysiology and Biofeedback, 35(1), 83–105.
Tolin, D. F., Davies, C. D., Moskow, D. M., & Hofmann, S. G. (2020b). Biofeedback and Neurofeedback for Anxiety Disorders: A Quantitative and Qualitative Systematic review. In Advances in experimental medicine and biology (pp. 265–289).
Mirifar, A., Beckmann, J., & Ehrlenspiel, F. (2017b). Neurofeedback as supplementary training for optimizing athletes’ performance: A systematic review with implications for future research. Neuroscience & Biobehavioral Reviews/Neuroscience and Biobehavioral Reviews, 75, 419–432.
Niv, S. (2013d). Clinical efficacy and potential mechanisms of neurofeedback. Personality and Individual Differences, 54(6), 676–686.
Linden, D. E., & Turner, D. L. (2016b). Real-time functional magnetic resonance imaging neurofeedback in motor neurorehabilitation. Current Opinion in Neurology, 29(4), 412–418.
Ros, T., Enriquez-Geppert, S., Zotev, V., Young, K. D., Wood, G., Whitfield-Gabrieli, S., Wan, F., Vuilleumier, P., Vialatte, F., Van De Ville, D., Todder, D., Surmeli, T., Sulzer, J. S., Strehl, U., Sterman, M. B., Steiner, N. J., Sorger, B., Soekadar, S. R., Sitaram, R., . . . Thibault, R. T. (2020c). Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist). Brain, 143(6), 1674–1685.
Lubar, J. F. (1991). Discourse on the development of EEG diagnostics and biofeedback for attention-deficit/hyperactivity disorders. Biofeedback and Self-regulation, 16(3), 201–225.
Larsen, S., & Sherlin, L. (2013b). Neurofeedback. Psychiatric Clinics of North America/the Psychiatric Clinics of North America, 36(1), 163–168.
Yang, X., Zeng, Y., Jiao, G., Gan, X., Linden, D., Hernaus, D., Zhu, C., Li, K., Yao, D., Yao, S., Jiang, Y., & Becker, B. (2024). A brief real-time fNIRS-informed neurofeedback training of the prefrontal cortex changes brain activity and connectivity during subsequent working memory challenge. Progress in Neuro-psychopharmacology & Biological Psychiatry, 110968.
Rydzik, Ł., Wąsacz, W., Ambroży, T., Javdaneh, N., Brydak, K., & Kopańska, M. (2023b). The Use of Neurofeedback in Sports Training: Systematic review. Brain Sciences, 13(4), 660.
Ciccarelli, G., Federico, G., Mele, G., Di Cecca, A., Migliaccio, M., Ilardi, C. R., Alfano, V., Salvatore, M., & Cavaliere, C. (2023). Simultaneous real-time EEG-fMRI neurofeedback: A systematic review. Frontiers in Human Neuroscience, 17.
Arns, M., Batail, J., Bioulac, S., Congedo, M., Daudet, C., Drapier, D., Fovet, T., Jardri, R., Le-Van-Quyen, M., Lotte, F., Mehler, D. M. A., Micoulaud-Franchi, J., Purper-Ouakil, D., & Vialatte, F. (2017). Neurofeedback: One of today’s techniques in psychiatry? L’Encéphale (Paris. En Ligne)/L’Encéphale, 43(2), 135–145.
Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., Weiskopf, N., Blefari, M. L., Rana, M., Oblak, E., Birbaumer, N., & Sulzer, J. (2016). Closed-loop brain training: the science of neurofeedback. Nature Reviews. Neuroscience, 18(2), 86–100.
Thibault, R. T., & Raz, A. (2017). The psychology of neurofeedback: Clinical intervention even if applied placebo. American Psychologist/the American Psychologist, 72(7), 679–688.
Rogala, J., Jurewicz, K., Paluch, K., Kublik, E., Cetnarski, R., & Wróbel, A. (2016). The Do’s and Don’ts of Neurofeedback Training: A review of the controlled studies using healthy adults. Frontiers in Human Neuroscience, 10.
Cortese, S., Ferrin, M., Brandeis, D., Holtmann, M., Aggensteiner, P., Daley, D., Santosh, P., Simonoff, E., Stevenson, J., Stringaris, A., Sonuga-Barke, E. J., Asherson, P., Banaschewski, T., Brandeis, D., Buitelaar, J., Coghill, D., Cortese, S., Daley, D., Danckaerts, M., . . . Zuddas, A. (2016c). Neurofeedback for Attention-Deficit/Hyperactivity Disorder: Meta-Analysis of clinical and neuropsychological outcomes from randomized controlled trials. Journal of the American Academy of Child and Adolescent Psychiatry, 55(6), 444–455.
Van Der Kolk, B. A., Hodgdon, H., Gapen, M., Musicaro, R., Suvak, M. K., Hamlin, E., & Spinazzola, J. (2016). A randomized controlled study of neurofeedback for chronic PTSD. PloS One, 11(12), e0166752.
Ordikhani-Seyedlar, M., Lebedev, M. A., Sørensen, H. B. D., & Puthusserypady, S. (2016b). Neurofeedback Therapy for Enhancing Visual Attention: State-of-the-Art and Challenges. Frontiers in Neuroscience, 10.
Koush, Y., Meskaldji, D., Pichon, S., Rey, G., Rieger, S. W., Linden, D. E., Van De Ville, D., Vuilleumier, P., & Scharnowski, F. (2015). Learning control over emotion networks through Connectivity-Based Neurofeedback. Cerebral Cortex, bhv311.
Haugg, A., Renz, F. M., Nicholson, A. A., Lor, C., Götzendorfer, S. J., Sladky, R., Skouras, S., McDonald, A., Craddock, C., Hellrung, L., Kirschner, M., Herdener, M., Koush, Y., Papoutsi, M., Keynan, J., Hendler, T., Kadosh, K. C., Zich, C., Kohl, S. H., . . . Steyrl, D. (2020b). Determinants of Real-Time FMRI Neurofeedback Performance and Improvement – a Machine Learning Mega-Analysis. bioRxiv (Cold Spring Harbor Laboratory).
Dehghani, A., Soltanian-Zadeh, H., & Hossein-Zadeh, G. (2020). Global Data-Driven Analysis of Brain Connectivity during Emotion Regulation by Electroencephalography Neurofeedback. Brain Connectivity, 10(6), 302–315.
Fovet, T., Micoulaud-Franchi, J., Vialatte, F., Lotte, F., Daudet, C., Batail, J., Mattout, J., Wood, G., Jardri, R., Enriquez-Geppert, S., & Ros, T. (2017). On assessing neurofeedback effects: should double-blind replace neurophysiological mechanisms? Brain, 140(10), e63.
Cortese, A., Amano, K., Koizumi, A., Kawato, M., & Lau, H. (2016b). Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance. Nature Communications, 7(1).
Young, K. D., Siegle, G. J., Zotev, V., Phillips, R., Misaki, M., Yuan, H., Drevets, W. C., & Bodurka, J. (2017). Randomized Clinical trial of Real-Time FMRI Amygdala Neurofeedback for Major Depressive Disorder: Effects on Symptoms and Autobiographical Memory Recall. the American Journal of Psychiatry, 174(8), 748–755.
Haugg, A., Sladky, R., Skouras, S., McDonald, A., Craddock, C., Kirschner, M., Herdener, M., Koush, Y., Papoutsi, M., Keynan, J. N., Hendler, T., Kadosh, K. C., Zich, C., MacInnes, J., Adcock, R. A., Dickerson, K., Chen, N., Young, K., Bodurka, J., . . . Scharnowski, F. (2020). Can we predict real‐time fMRI neurofeedback learning success from pretraining brain activity? Human Brain Mapping, 41(14), 3839–3854.
Hohenfeld, C., Nellessen, N., Dogan, I., Kuhn, H., Müller, C., Papa, F., Ketteler, S., Goebel, R., Heinecke, A., Shah, N. J., Schulz, J. B., Reske, M., & Reetz, K. (2017). Cognitive Improvement and Brain Changes after Real-Time Functional MRI Neurofeedback Training in Healthy Elderly and Prodromal Alzheimer’s Disease. Frontiers in Neurology, 8.
Mehran, Y. Z., Firoozabadi, M., & Rostami, R. (2014). Improvement of neurofeedback therapy for improved attention through facilitation of brain activity using local sinusoidal extremely low frequency magnetic field exposure. Clinical EEG and Neuroscience, 46(2), 100–112.
Schabus, M., Griessenberger, H., Gnjezda, M., Heib, D. P. J., Wislowska, M., & Hoedlmoser, K. (2017). Better than sham? A double-blind placebo-controlled neurofeedback study in primary insomnia. Brain, 140(4), 1041–1052.
Scharnowski, F., Hutton, C., Josephs, O., Weiskopf, N., & Rees, G. (2012). Improving Visual Perception through Neurofeedback. the Journal of Neuroscience/the Journal of Neuroscience, 32(49), 17830–17841.
Alkoby, O., Abu-Rmileh, A., Shriki, O., & Todder, D. (2018). Can we predict who will respond to neurofeedback? A review of the inefficacy problem and existing predictors for successful EEG Neurofeedback learning. Neuroscience, 378, 155–164.
Thompson, L., & Thompson, M. (2023). Effective intervention for attention-deficit/hyperactivity disorder using quantitative electroencephalography and neurofeedback. In Elsevier eBooks (pp. 375–396).
Arns, M., De Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2009). Efficacy of Neurofeedback Treatment in ADHD: The Effects on inattention, impulsivity and Hyperactivity: A Meta-Analysis. Clinical EEG and Neuroscience, 40(3), 180–189.
Weiskopf, N. (2012). Real-time fMRI and its application to neurofeedback. NeuroImage, 62(2), 682–692.
Prinsloo, G. E., Rauch, H. L., & Derman, W. E. (2014). A brief review and clinical application of heart rate variability biofeedback in sports, exercise, and rehabilitation medicine. Physician and Sportsmedicine, 42(2), 88–99.
Shibata, K., Lisi, G., Cortese, A., Watanabe, T., Sasaki, Y., & Kawato, M. (2019d). Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback. NeuroImage, 188, 539–556.
Masterpasqua, F., & Healey, K. N. (2003c). Neurofeedback in psychological practice. Professional Psychology, Research and Practice, 34(6), 652–656.
Thibault, R. T., Lifshitz, M., & Raz, A. (2017). The climate of neurofeedback: scientific rigour and the perils of ideology. Brain, 141(2), e11.
Sherlin, L. H., Arns, M., Lubar, J., Heinrich, H., Kerson, C., Strehl, U., & Sterman, M. B. (2011). Neurofeedback and Basic Learning Theory: Implications for research and practice. Journal of Neurotherapy, 15(4), 292–304.
Jiang, Y., Abiri, R., & Zhao, X. (2017). Tuning Up the Old Brain with New Tricks: Attention Training via Neurofeedback. Frontiers in Aging Neuroscience, 9.
Omejc, N., Rojc, B., Battaglini, P. P., & Marusic, U. (2018c). Review of the therapeutic neurofeedback method using electroencephalography: EEG Neurofeedback. Bosnian Journal of Basic Medical Sciences.
Kohl, S. H., Mehler, D. M. A., Lührs, M., Thibault, R. T., Konrad, K., & Sorger, B. (2020). The potential of Functional Near-Infrared Spectroscopy-Based Neurofeedback—A Systematic Review and recommendations for Best practice. Frontiers in Neuroscience, 14.
Enriquez-Geppert, S., Huster, R. J., & Herrmann, C. S. (2017). EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A review tutorial. Frontiers in Human Neuroscience, 11.

The authors declare no competing interests.

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Neurofeedback: Applications, Advancements, and Future Directions

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Abstract

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Significance Statement

1. Introduction

2. Methods

3. Historical Background of Neurofeedback

4. Methods and Techniques in Neurofeedback

5. Applications of Neurofeedback

6. Advancements and Innovations in Neurofeedback

7. Key Findings and Efficacy of Neurofeedback

8. Limitations and Challenges

9. Future Directions and Potential of Neurofeedback

Conclusion

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Conflict of Interest Statement

References

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