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EEG neurofeedback research: A fertile ground for psychiatry?
Abstract
The clinical efficacy of neurofeedback is still a matter of debate. This paper analyzes the factors that should be taken into account in a transdisciplinary approach to evaluate the use of EEG NFB as a therapeutic tool in psychiatry. Neurofeedback is a neurocognitive therapy based on human–computer interaction that enables subjects to train voluntarily and modify functional biomarkers that are related to a defined mental disorder. We investigate three kinds of factors related to this definition of neurofeedback. We focus this article on EEG NFB. The first part of the paper investigates neurophysiological factors underlying the brain mechanisms driving NFB training and learning to modify a functional biomarker voluntarily. Two kinds of neuroplasticity involved in neurofeedback are analyzed: Hebbian neuroplasticity, i.e. long-term modification of neural membrane excitability and/or synaptic potentiation, and homeostatic neuroplasticity, i.e. homeostasis attempts to stabilize network activity. The second part investigates psychophysiological factors related to the targeted biomarker. It is demonstrated that neurofeedback involves clearly defining which kind of relationship between EEG biomarkers and clinical dimensions (symptoms or cognitive processes) is to be targeted. A nomenclature of accurate EEG biomarkers is proposed in the form of a short EEG encyclopedia (EEGcopia). The third part investigates human–computer interaction factors for optimizing NFB training and learning during the closed loop interaction. A model is proposed to summarize the different features that should be controlled to optimize learning. The need for accurate and reliable metrics of training and learning in line with human–computer interaction is also emphasized, including targeted biomarkers and neuroplasticity. All these factors related to neurofeedback show that it can be considered as a fertile ground for innovative research in psychiatry.
... While some studies have shown that significant improvements occur 75-80% of the time (Hammond, 2011), the clinical efficacy of neurofeedback is still debated. Neurofeedback's potential is, however, recognised in innovative therapy and psychiatric research (Batail et al., 2019). ...
... There is growing evidence that neuroplasticity could be used to promote healing and recovery in critical brain functions (Kays et al., 2012) and, therefore, overall mental well-being. Neurofeedback is a neurocognitive therapy closely related to neuroplasticity as it helps patients learn to regulate their brain's electrical activity (Batail et al., 2019). It has been lauded as a powerful method to trigger brain plasticity (Loriette et al., 2021), harnessing electroencephalography (EEG), a technique used to study the electrical activity of the brain as well as detect abnormal patterns in brain activity (Cohen, 2017). ...
Abstract
“Believe it or not, you really can change your brain” (Cohen M, 2020, p.27).
Background: Mental health concerns are increasing worldwide, and these can substantially affect all areas of life (World Health Organisation, 2022). Prior studies have indicated that neurofeedback therapy has significantly improved mental health 75-80% of the time (Hammond, 2011). This study aims to understand, through participants' experiences and perceptions, whether neurofeedback therapy has an impact on mental well-being.
Method: A qualitative study using thematic analysis was conducted with eight individuals who participated in neurofeedback therapy between 2020 and 2023.
Results: A thematic analysis (Braun & Clarke, 2006) identified themes consistent across all participants relating to the positive impact of neurofeedback therapy, particularly, broad themes that described the therapy as “life-changing”, “having a positive impact on brain health” and a strong belief that this therapy could “benefit others”.
Conclusion: This research validates that the experiences of neurofeedback participants are congruent with the findings of previous studies on neurofeedback and offer opportunities for future research highlighting the positive impact of neurofeedback therapy and improving the accessibility of this ground-breaking therapy.
... Neurofeedback is one such promising strategy, relying on a training approach to achieve conscious selfmodulation of specific brain waves on the basis of realtime feedback [4][5][6][7]. For example, neurofeedback training, through electroencephalography (EEG) passive Brain-Computer Interfaces (BCI), has been found to improve attention in the elderly when focused on the Peak Alpha Frequency (PAF) -a neural biomarker of aging [8][9][10]. ...
... Data cleaning includes space separation to remove eye blink and saccade artifacts. Then, EEG signals were notchfiltered at 50 Hz to remove the power line noise and bandpass-filtered in the four frequency bands corresponding to theta [4][5][6][7][8], alpha [8][9][10][11][12][13], beta [13][14][15][16][17][18][19][20][21][22][23][24][25], and gamma [35][36][37][38][39][40][41][42][43][44][45] bands. The filtered data, which had a total duration of 75 seconds per acquisition and per subject, were segmented into 2-second epochs. ...
Background: Electroencephalography (EEG) stands as a pivotal non-invasive tool, capturing brain signals with millisecond precision and enabling real-time monitoring of individuals' mental states. Using appropriate biomarkers extracted from these EEG signals and presenting them back in a neurofeedback loop offers a unique avenue for promoting neural compensation mechanisms. This approach empowers individuals to skillfully modulate their brain activity. Recent years have witnessed the identification of neural biomarkers associated with aging, underscoring the potential of neuromodulation to regulate brain activity in the elderly. Methods and Objectives: Within the framework of an EEG-based brain-computer interface, this study focused on three neural biomarkers that may be disturbed in the aging brain: Peak Alpha Frequency, Gamma-band synchronization, and Theta/Beta ratio. The primary objectives were twofold: (1) to investigate whether elderly individuals with subjective memory complaints can learn to modulate their brain activity, through EEG-neurofeedback training, in a rigorously designed double-blind, placebo-controlled study; and (2) to explore potential cognitive enhancements resulting from this neuromodulation. Results: A significant self-modulation of the Gamma-band synchronization biomarker, critical for numerous higher cognitive functions and known to decline with age, and even more in Alzheimer's disease (AD), was exclusively observed in the group undergoing EEG-neurofeedback training. This effect starkly contrasted with subjects receiving sham feedback. While this neuromodulation did not directly impact cognitive abilities, as assessed by pre-versus post-training neuropsycho-logical tests, the high baseline cognitive performance of all subjects at study entry likely contributed to this result. Conclusion: The findings of this double-blind study align with a key criterion for successful neuromodulation, highlighting the significant potential of Gamma-band synchronization in such a process. This important outcome encourages further exploration of EEG-neurofeedback on this specific neural biomarker as a promising intervention to counter the cognitive decline that often accompanies brain aging and, eventually, to modify the progression of AD.
... (a) Optimizing NF by improving the overall brain state and readiness for learning shows good feasibility NF shows potential for clinical applications, with ongoing advancements in methodology for precise brain activity identification and extraction [31]. Additionally, human-computer/human-factor approaches consider task-specific factors, cognitive/motivational traits and technology acceptance for NF optimization [101]. Complementary to these efforts, our study focuses moreover on enhancing the overall brain state and its readiness for learning, aiming to boost neuroplasticity to amplify NF's impact: the current approach therefore combines fm-theta NF with psilocybin. ...
Executive function deficits, common in psychiatric disorders, hinder daily activities and may be linked to diminished neural plasticity, affecting treatment and training responsiveness. In this pioneering study, we evaluated the feasibility and preliminary efficacy of psilocybin-assisted frontal-midline theta neurofeedback (NF), a neuromodulation technique leveraging neuroplasticity, to improve executive functions (EFs). Thirty-seven eligible participants were randomized into an experimental group (n = 18) and a passive control group (n = 19). The experimental group underwent three microdose sessions and then three psilocybin-assisted NF sessions, without requiring psychological support, demonstrating the approach’s feasibility. NF learning showed a statistical trend for increases in frontal-midline theta from session to session with a large effect size and non-significant but medium effect size dynamical changes within sessions. Placebo effects were consistent across groups, with no tasks-based EF improvements, but significant self-reported gains in daily EFs—working memory, shifting, monitoring and inhibition—showing medium and high effect sizes. The experimental group’s significant gains in their key training goals underscored the approach’s external relevance. A thorough study with regular sessions and an active control group is crucial to evaluate EFs improvement and their specificity in future. Psilocybin-enhanced NF could offer significant, lasting benefits across diagnoses, improving daily functioning.
This article is part of the theme issue ‘Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation’.
... New mobile systems can sometimes offer a good trade-off between mobility needs for the participants and accuracy/richness of the data for the experimenter, with some devices reaching as many as 64 channels. Moreover, the wireless communication they offer has allowed researchers to use mobile EEG devices in very different contexts, from the development of brain-computer interfaces (BCIs) [5,6], to neurofeedback protocols [7,8,9]. ...
Advancements in brain imaging technologies have facilitated the development of “real-world” experimental scenarios. In this study, participants engaged in a household chore – completing a laundry cycle – while their frontal lobe brain activity was monitored using fNIRS. Participants completed this twice using both fragranced and unfragranced detergent, to explore if fNIRS is able to identify any differences in brain activity in response to subtle changes in stimuli. Analysis was conducted using Automatic IDentification of functional Events (AIDE) software and fNIRS correlation-based signal improvement (CBSI). Results indicated that brain activity, particularly in the right frontopolar and occasionally the left dorsolateral prefrontal cortex, was more pronounced and frequent with the unfragranced detergent than the fragranced. This suggests that completing tasks in an environment where a pleasant and relaxing fragrance is present might be less effortful compared to an odourless environment.
... While this meta-analysis primarily focuses on continuous frequency band training in EEG-neurofeedback studies, Batail et al. (2019) stress the significance of basing neurofeedback on specific well-known biomarkers or on solid neurophysiological principles (e.g., EEG coherence, Li et al., 2019; event-related brain potentials (ERPs), Cavanagh and Frank, 2014;Cavanagh and Shackman, 2015). However, in contrast to protocols with continuous feedback, ERP-based protocols would require intermittent feedback on the basis of specific trials, which overall implies that less frequent feedback is provided. ...
... These research results were the first step to developing NFB, which has been widely used to treat psychiatric disorders [4], improve cognitive function [5], enhance motor performance [6], and so on. In NFB, brain activity is measured mainly by EEG [7], functional magnetic resonance imaging (fMRI) [8], and functional near-infrared spectroscopy [9], and feedback on the activity in the targeted brain regions is provided to the participant to facilitate selfregulation of the participant's brain activity. The methods used to provide feedback include visual feedback [10], in which brain activity is projected on a monitor, and auditory feedback [11], which uses sound stimuli of different frequencies. ...
Neurofeedback (NFB) is a closed-loop technique in which the patient receives feedback on brain activity to encourage voluntary control of brain activity. NFB promotes neuroplasticity and changes the brain functionally and structurally. Motor imagery-based NFB (MI-NFB) can improve motor imagery ability by providing feedback on brain activity during motor imagery, thereby showing effectiveness in performance and motor learning. Furthermore, the effects of MI-NFB are further enhanced when it is combined with noninvasive brain stimulation and motor exercise. Therefore, MI-NFB is used in the physiotherapy of patients with neurological diseases, such as stroke and Parkinson disease, as well as children with attention deficit-hyperactivity disorder and elderly people. This chapter reviews MI-NFB in physiotherapy practice, thus contributing to the development of effective evidence-based physiotherapy.
... The rationale behind neurofeedback is that precise neural changes induced by training result in corresponding behavioural improvements (Sitaram et al., 2017). Neurofeedback protocols, often based on modulating spontaneous brain rhythms (Batail et al., 2019;Marzbani et al., 2016), have shown promise in various mental disorders, such as ADs and hyperactivity disorder, developmental delay, learning disabilities, sleep problems, anxiety disorders and posttraumatic stress disorder (Breteler et al., 2010;Domschke et al., 2010;Lubar et al., 1995;Micoulaud-Franchi et al., 2021;Monastra et al., 2005;Surmeli & Ertem, 2010). ...
Background
Limited research exists regarding the effectiveness of electroencephalogram (EEG) neurofeedback training for children with cerebral palsy (CP) and co-occurring attention deficits (ADs), despite the increasing prevalence of these dual conditions. This study aimed to fill this gap by examining the impact of neurofeedback training on the attention levels of children with CP and AD.
Methods
Nineteen children with both CP and co-occurring ADs were randomly assigned to either a neurofeedback or control group. The neurofeedback group received 20 sessions of training, lasting approximately 1 h per day, twice a week. Theta/beta ratios of the quantitative electroencephalography (QEEG) recordings were measured pre-training and post-training in the resting state. The Continuous Performance Test (CPT), the Test of Visual Perceptual Skills—3rd Version (TVPS-3) and the Conners' Parent Rating Scale (CPRS) were measured at pre- and post-training.
Results
The neurofeedback group showed both decreased theta/beta ratios compared with control group (p = 0.04) at post-training and a within-group improvement during training (p = 0.02). Additionally, the neurofeedback group had a trend of decreased omission rates of the CPT (p = 0.08) and the visual sequential memory and the visual closure subscores in the TVPS-3, compared with the control group (p = 0.02 and p = 0.01, respectively).
Conclusions
The results suggested that children with CP and co-occurring AD may benefit from neurofeedback training in their attention level. Further research is needed to explore long-term effects and expand its application in this population.
Neurofeedback allows individuals to monitor and self-regulate their brain activity, potentially improving human brain function. Beyond the traditional electrophysiological approach using primarily electroencephalography, brain haemodynamics measured with functional magnetic resonance imaging (fMRI) and more recently, functional near-infrared spectroscopy (fNIRS) have been used (haemodynamic-based neurofeedback), particularly to improve the spatial specificity of neurofeedback. Over recent years, especially fNIRS has attracted great attention because it offers several advantages over fMRI such as increased user accessibility, cost-effectiveness and mobility—the latter being the most distinct feature of fNIRS. The next logical step would be to transfer haemodynamic-based neurofeedback protocols that have already been proven and validated by fMRI to mobile fNIRS. However, this undertaking is not always easy, especially since fNIRS novices may miss important fNIRS-specific methodological challenges. This review is aimed at researchers from different fields who seek to exploit the unique capabilities of fNIRS for neurofeedback. It carefully addresses fNIRS-specific challenges and offers suggestions for possible solutions. If the challenges raised are addressed and further developed, fNIRS could emerge as a useful neurofeedback technique with its own unique application potential—the targeted training of brain activity in real-world environments, thereby significantly expanding the scope and scalability of haemodynamic-based neurofeedback applications.
This article is part of the theme issue ‘Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation’.
Neurofeedback is a promising treatment for children with ADHD. However, although several studies have investigated its efficacy, the effectiveness of current approaches is still debated. This might be partly due to the biomarkers that are used and might not be enough specific of ADHD core symptoms. We here motivate the evaluation of P300-based BCI training as an alternative. We review the arguments in favor of this approach and reveal the design of an ongoing randomized and controlled clinical trial. Essentially, the P300 EEG evoked response is affected in ADHD. It does reflect selective attention and action selection. It is modulated by successful pharmacological intervention in ADHD. And it can be used in BCI for training purposes, through varied and engaging games. Interestingly, these games enable the use of precise instructions as well as multi-level feedbacks to favor learning. Finally, this new type of Neurofeedback allows for instantiating a highly specific control condition that is compatible with a double-blind design.
Functional magnetic resonance imaging neurofeedback (fMRI-NF) training of areas involved in emotion processing can reduce depressive symptoms by over 40% on the Hamilton Depression Rating Scale (HDRS). However, it remains unclear if this efficacy is specific to
feedback from emotion-regulating regions. We tested in a single-blind, randomised, controlled trial if upregulation of emotion areas (NFE) yields superior efficacy compared to upregulation of a control region activated by visual scenes (NFS). Forty-three moderately to
severely depressed medicated patients were randomly assigned to five sessions augmentation treatment of either NFE or NFS training. At primary outcome (week 12) no significant group mean HDRS difference was found (B = -0.415 [95% CI -4.847 to 4.016], p = 0.848) for the 32 completers (16 per group). However, across groups depressive symptoms decreased by 43%, and 38% of patients remitted. These improvements lasted until follow-up (week 18).
Both groups upregulated target regions to a similar extent. Further, clinical improvement was correlated with an increase in self-efficacy scores. However, the interpretation of clinical improvements remains limited due to lack of a sham-control group. We thus surveyed effects
reported for accepted augmentation therapies in depression. Data indicated that our findings exceed expected regression to the mean and placebo effects that have been reported for drug trials and other sham-controlled high-technology interventions. Taken together, we suggest that the experience of successful self-regulation during fMRI-NF training may be therapeutic. We conclude that if fMRI-NF is effective for depression, self-regulation training of higher visual areas may provide an effective alternative.
Objective:
While promising for many applications, Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are still scarcely used outside laboratories, due to a poor reliability. It is thus necessary to study and fix this reliability issue. Doing so requires the use of appropriate reliability metrics to quantify both the classification algorithm and the BCI user's performances. So far, Classification Accuracy (CA) is the typical metric used for both aspects. However, we argue in this paper that CA is a poor metric to study BCI users' skills. Here, we propose a definition and new metrics to quantify such BCI skills for Mental Imagery (MI) BCIs, independently of any classification algorithm.
Approach: We first show in this paper that CA is notably unspecific, discrete, training data and classifier dependent, and as such may not always reflect successful self-modulation of EEG patterns by the user. We then propose a definition of MI-BCI skills that reflects how well the user can self-modulate EEG patterns, and thus how well he could control an MI-BCI. Finally, we propose new performance
metrics, classDis, restDist and classStab that specifically measure how distinct and stable the EEG patterns produced by the user are, independently of any classifier.
Main results: By re-analyzing EEG data sets with such new metrics, we indeed confirmed that CA may hide some increase in MI-BCI skills or hide the user inability to self-modulate a given EEG pattern. On the other hand, our new metrics could reveal such skill improvements as well as identify when a mental task performed by a user was no different than rest EEG.
Significance: Our results showed that when studying MI-BCI users' skills, CA should be used with care, and complemented with metrics such as the new ones proposed. Our results also stressed the need to redefine BCI user training by considering the different BCI subskills and their measures. To promote the complementary use of our new metrics, we provide the Matlab code to compute them for free and open-source.
In their recent paper, Alkoby et al. (2017) provide the readership with an extensive and very insightful review of the factors influencing NeuroFeedback (NF) performance. These factors are drawn from both the NF literature and the Brain-Computer Interface (BCI) literature. Our short review aims to complement Alkoby et al.'s review by reporting recent additions to the BCI literature. The object is to highlight this literature and discuss its potential relevance and usefulness to better understand the processes underlying NF and further improve the design of clinical trials assessing NF efficacy. Indeed, we are convinced that while NF and BCI are fundamentally different in many ways, both the BCI and NF communities could reach compelling achievements by building upon one another. By reviewing the recent BCI literature, we identified three types of factors that influence BCI performance: task-specific, cognitive/motivational and technology-acceptance related factors. Since BCIs and neurofeedback share a common goal (i.e., learning to modulate specific neurophysiological patterns), similar cognitive and neurophysiological processes are likely to be involved during the training process. Thus, the literature on BCI training may help (1) to deepen our understanding of neurofeedback training processes and (2) to understand the variables that influence the clinical efficacy of NF. This may help to properly assess and/or control the influence of these variables during randomised controlled trials.
Objective:
Most current Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately 10 years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs.
Approach:
We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons.
Main results:
We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers.
Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods.
Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods.
Significance:
This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.
Novel methods that stimulate neuroplasticity are increasingly being studied to treat neurological and psychiatric conditions. We sought to determine whether real-time fMRI neurofeedback training is feasible in Huntington's disease (HD), and assess any factors that contribute to its effectiveness. In this proof-of-concept study, we used this technique to train 10 patients with HD to volitionally regulate the activity of their supplementary motor area (SMA). We collected detailed behavioral and neuroimaging data before and after training to examine changes of brain function and structure, and cognitive and motor performance. We found that patients overall learned to increase activity of the target region during training with variable effects on cognitive and motor behavior. Improved cognitive and motor performance after training predicted increases in pre-SMA grey matter volume, fMRI activity in the left putamen, and increased SMA–left putamen functional connectivity. Although we did not directly target the putamen and corticostriatal connectivity during neurofeedback training, our results suggest that training the SMA can lead to regulation of associated networks with beneficial effects in behavior. We conclude that neurofeedback training can induce plasticity in patients with Huntington's disease despite the presence of neurodegeneration, and the effects of training a single region may engage other regions and circuits implicated in disease pathology.
Motor imagery (MI) with neurofeedback has been suggested as promising for motor recovery after stroke. Evidence suggests that regular training facilitates compensatory plasticity, but frequent training is difficult to integrate into everyday life. Using a wireless electroencephalogram (EEG) system, we implemented a frequent and efficient neurofeedback training at the patients' home. Aiming to overcome maladaptive changes in cortical lateralization patterns we presented a visual feedback, representing the degree of contralateral sensorimotor cortical activity and the degree of sensorimotor cortex lateralization. Three stroke patients practiced every other day, over a period of 4 weeks. Training-related changes were evaluated on behavioral, functional, and structural levels. All 3 patients indicated that they enjoyed the training and were highly motivated throughout the entire training regime. EEG activity induced by MI of the affected hand became more lateralized over the course of training in all three patients. The patient with a significant functional change also showed increased white matter integrity as revealed by diffusion tensor imaging, and a substantial clinical improvement of upper limb motor functions. Our study provides evidence that regular, home-based practice of MI neurofeedback has the potential to facilitate cortical reorganization and may also increase associated improvements of upper limb motor function in chronic stroke patients.
Many Brain Computer Interface (BCI) and neurofeedback studies have investigated the impact of sensorimotor rhythm (SMR) self-regulation training procedures on motor skills enhancement in healthy subjects and patients with motor disabilities. This critical review aims first to introduce the different definitions of SMR EEG target in BCI/Neurofeedback studies and to summarize the background from neurophysiological and neuroplasticity studies that led to SMR being considered as reliable and valid EEG targets to improve motor skills through BCI/neurofeedback procedures. The second objective of this review is to introduce the main findings regarding SMR BCI/neurofeedback in healthy subjects. Third, the main findings regarding BCI/neurofeedback efficiency in patients with hypokinetic activities (in particular, motor deficit following stroke) as well as in patients with hyperkinetic activities (in particular, Attention Deficit Hyperactivity Disorder, ADHD) will be introduced. Due to a range of limitations, a clear association between SMR BCI/neurofeedback training and enhanced motor skills has yet to be established. However, SMR BCI/neurofeedback appears promising, and highlights many important challenges for clinical neurophysiology with regards to therapeutic approaches using BCI/neurofeedback.