Introduction

The American Psychological Association’s October 2022 survey indicated that 76% of American adults reported levels of stress and anxiety which negatively impacted their quality of life daily. Prolonged periods of high stress have been correlated with several degenerative effects on overall health, including the reduction of gray matter in the prefrontal cortex, cardiovascular dysfunctions, diabetes, cancer, autoimmune syndromes, and mental divergences such as depression and anxiety disorders (Mariotti, 2015).

36% of polled adults stated that they “don’t know where to start when it comes to managing their stress” (American Psychological Association, 2022). Many individuals resort to destructive behaviors such as alcoholism, drug abuse, and other high-risk activities as coping mechanisms (McEwen, 2008), while others are prescribed pharmacological interventions which come with unintended side effects and high rates of failure (Perez et al., 2021).

Quantitative electroencephalography (qEEG) neurofeedback (NF / NFB) offers a cheap and potentially effective alternative to combating stress. Following the usage of electroencephalography for diagnosing various conditions such as seizures and sleeping disorders in clinical settings, it was recognized that the readings provided extensive insight into several cortical markers on the biological (rather than psychological) level. As technology progressed, the ability to identify, display, store, and analyze these patterns came in the form of qEEG algorithms. Quantitative electroencephalography is a technique that analyzes cortical activity by transforming raw EEG data into digitized models, offering a detailed assessment of brain wave patterns (Kaizer, 2016). Neurofeedback, or EEG biofeedback, was developed in the late 1960s as a method to “train” brain waves utilizing qEEG readings and operant conditioning. Since then, extensive research has been conducted to examine the versatility and efficacy of neurofeedback. The results have shown promise in treating conditions such as anxiety, ADHD/ADD, and PTSD (Hammond, 2007). The significance of this method lies in its non-pharmacological approach, which addresses reliance and addiction-related concerns associated with medications. Additionally, its accessibility as a cost-effective outpatient treatment option is similarly beneficial, making it a valuable resource in neuroscience-based clinics and mental health care facilities (Keizer, 2021).

With increasing concerns revolving around stress-related ailments in our modern world, qEEG-based neurofeedback offers a promising direction for non-pharmacological treatment of generalized stress within the wider population. This focus explores the frontier of positive psychology, which not only aims to assist individuals with disorders but also targets the general population, striving to better the human condition as a whole. The following report investigates the use of various qEEG treatment variables including reference schemes, personalization of treatment plans, target frequency modulation, and methods of feedback delivery for real-time intervention of stress-related symptoms by analyzing relevant scientific literature. To the best of our knowledge, modern studies utilizing qEEG treatments primarily aim to relieve stress-related symptoms stemming from conditions such as Post Traumatic Stress Disorder (PTSD), Major Depressive Disorder (MDD), and Generalized Anxiety Disorder (GED), creating a distinct knowledge gap of applicability between the treatment of disorders and relieving daily stress.

Methods:

We aimed to catalog a wide variety of relevant articles related to stress, neurofeedback, and qEEG efficacy. This goal aided in ensuring the minimization of selection bias and facilitated the consideration of several schools of thought during our research. The literary search was conducted by querying open-access databases using the following keywords: stress, neurofeedback, qEEG-guided neurofeedback, alpha asymmetry, and electroencephalography. Articles compiled were sourced from the journals of Neuroregulation, Science Direct, IEEE Access, ResearchGate, Frontiers in Psychology, Taylor Francis Online, BMC Psychiatry, and Future Science OA.

In total, we analyzed nineteen documents from the years 2008 to 2022 that met our eligibility criteria. The inclusion and exclusion criteria were determined according to the relevance to the topics analyzed and the content of the results provided. While we referenced various studies that introduced complexity by utilizing qEEG neurofeedback along with other treatment methods, such papers were excluded from our investigation as the required analysis was beyond the scope of this paper. Selected documents were subsequently mined for information pertinent to treatment methodologies, predisposing demographic factors, and novel results.

Results:

Quantitative electroencephalography-based neurofeedback shows promise in bringing stress-relieving treatment plans to the greater population. This potential is founded by the technique’s high temporal resolution which enables the mechanisms central to operant conditioning. The usage of the 10-20 electrode placement system and the verification of fewer than 5 ohms of impedance were seen in multiple studies within our review (Jones & Hitsman, 2018; Dreis et al., 2015; Hafeez et al., 2019), both of which ensure results are valid and are transferable across the field (Bruchim et al., 2023). Personalized treatment plans were often created by comparing participants’ baseline qEEG readings to a normative database, creating initial z scores that indicate the amount of deviation from normative readings. These treatment plans were then implemented by providing neurofeedback via games, animations, sounds, and analog presentations which aided participants in either upregulating or downregulating certain brainwave amplitudes. Alpha asymmetry, high frontal beta, and sensorimotor rhythm (SMR) were the most common markers of stress within the reviewed studies, which often had treatment protocols in place aimed to regulate either one or a multitude of these indicators (Jones & Hitsman, 2018; Dreis et al., 2015; Hafeez et al., 2019; White et al., 2017).

The mediums with which neurofeedback was presented to participants often included visual references, audio recordings, or interactive games (which may utilize both auditory and visual cues). Hafeez and colleagues (2019) concluded that the most effective stimuli for neurofeedback within stress-related scenarios were three-dimensional computer games. Combined with the findings of Du Bois et al., (2021), it would be reasonable to conclude that 3D games which rely on participants’ regulation - rather than intentional modulation - of brain activity would result in the best-performing neurofeedback stimulus.

Discussion:

Contextualizing our results within the broader context of EEG-NFB research as a whole is important to ensure this study is interpreted correctly and can best guide future research. Overall, statistically significant survey results collected pre and post-treatment provide strong evidence for this technique. This conclusion supports previous research on the topic and provides relevant insight into the methodologies used to conduct the research. The lack of control groups, multiple methods for feedback transmission, sparse session attendance, low subject counts, and lack of changes within qEEG readings, however, should be considered when interpreting many of these studies. EEG neurofeedback is a relative niche treatment for disordered behaviors as pharmacological and talk-based therapies prevail in our modern world. While these treatments may prove effective for some, neurofeedback-based solutions may become just as if not more popularized with the advent of better computational algorithms and increased processing power. Moreover, EEG-NFB targets disorders on a biological rather than psychological level, allowing for better standardization of treatment protocols and the targeting of root causes rather than symptoms. The lack of generalized utilization of NFB treatment limits many studies’ sizes and funding, leading to smaller sample sizes and infrequent session attendance by participants. As the field grows, the aforementioned disadvantages will be accounted for and clearer results will be obtained.

This report provides a foundation for future research on the generalized treatment of stress using EEG-NFB. To best make use of this report, several factors should be kept in mind. Our study extrapolated results from studies focused on treating anxiety-related symptoms in disorders such as GED, PTSD, and MDD. While the markers and treatments for anxiety caused by these conditions may prove similar to generalized stress (not caused by predisposing conditions), further research is needed to verify this fact. Additionally, these protocols may not apply to patients with non-neurotypical statuses such as Autism Spectrum Disorder and Traumatic Brain Injury victims as they may require alternate normative databases for treatment plan construction.

Conclusion:

Managing and treating stress in our world of productivity, social media, and widespread uncertainty will likely prove to be an immense task with no single solution. While pharmaceutical interventions and traditional therapy techniques may improve over time, the rising popularity and effectiveness of qEEG-based neurofeedback may provide a great direction for stress mitigation. This report can act as a reference document for future research as it includes various results, methodologies, and setbacks to be addressed in experimental environments. Overall, stress mitigation via EEG-NFB should be further researched, and subsequent studies may benefit from examining variables such as the best markers of stress, the most optimal scheduling regimens, and the efficacy of novel stimulus modalities such as wearable devices.