Self-Modulation of Gamma-Band Synchronization through EEG-Neurofeedback Training in the Elderly.

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 neuropsychological 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.

Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist).

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

Towards a Pragmatic Approach to a Psychophysiological Unit of Analysis for Mental and Brain Disorders: An EEG-Copeia for Neurofeedback.

This article proposes what we call an "EEG-Copeia" for neurofeedback, like the "Pharmacopeia" for psychopharmacology. This paper proposes to define an "EEG-Copeia" as an organized list of scientifically validated EEG markers, characterized by a specific association with an identified cognitive process, that define a psychophysiological unit of analysis useful for mental or brain disorder evaluation and treatment. A characteristic of EEG neurofeedback for mental and brain disorders is that it targets a EEG markers related to a supposed cognitive process, whereas conventional treatments target clinical manifestations. This could explain why EEG neurofeedback studies encounter difficulty in achieving reproducibility and validation. The present paper suggests that a first step to optimize EEG neurofeedback protocols and future research is to target a valid EEG marker. The specificity of the cognitive skills trained and learned during real time feedback of the EEG marker could be enhanced and both the reliability of neurofeedback training and the therapeutic impact optimized. However, several of the most well-known EEG markers have seldom been applied for neurofeedback. Moreover, we lack a reliable and valid EEG targets library for further RCT to evaluate the efficacy of neurofeedback in mental and brain disorders. With the present manuscript, our aim is to foster dialogues between cognitive neuroscience and EEG neurofeedback according to a psychophysiological perspective. The primary objective of this review was to identify the most robust EEG target. EEG markers linked with one or several clearly identified cognitive-related processes will be identified. The secondary objective was to organize these EEG markers and related cognitive process in a psychophysiological unit of analysis matrix inspired by the Research Domain Criteria (RDoC) project.

Independent Vector Analysis for SSVEP Signal Enhancement, Detection, and Topographical Mapping.

Steady state visual evoked potentials (SSVEPs) have been identified as an effective solution for brain computer interface (BCI) systems as well as for neurocognitive investigations. SSVEPs can be observed in the scalp-based recordings of electroencephalogram signals, and are one component buried amongst the normal brain signals and complex noise. We present a novel method for enhancing and improving detection of SSVEPs by leveraging the rich joint blind source separation framework using independent vector analysis (IVA). IVA exploits the diversity within each dataset while preserving dependence across all the datasets. This approach is shown to enhance the detection of SSVEP signals across a range of frequencies and subjects for BCI systems. Furthermore, we show that IVA enables improved topographic mapping of the SSVEP propagation providing a promising new tool for neuroscience and neurocognitive research.

A novel method of early diagnosis of Alzheimer's disease based on EEG signals.

Studies have reported that electroencephalogram signals in Alzheimer's disease patients usually have less synchronization than those of healthy subjects. Changes in electroencephalogram signals start at early stage but, clinically, these changes are not easily detected. To detect this perturbation, three neural synchrony measurement techniques: phase synchrony, magnitude squared coherence, and cross correlation are applied to three different databases of mild Alzheimer's disease patients and healthy subjects. We have compared the right and left temporal lobes of the brain with the rest of the brain areas (frontal, central, and occipital) as temporal regions are relatively the first ones to be affected by Alzheimer's disease. Moreover, electroencephalogram signals are further classified into five different frequency bands (delta, theta, alpha beta, and gamma) because each frequency band has its own physiological significance in terms of signal evaluation. A new approach using principal component analysis before applying neural synchrony measurement techniques has been presented and compared with Average technique. The simulation results indicated that applying principal component analysis before synchrony measurement techniques shows significantly better results as compared to the lateral one. At the end, all the aforementioned techniques are assessed by a statistical test (Mann-Whitney U test) to compare the results.

Towards Semi-Automatic Artifact Rejection for the Improvement of Alzheimer's Disease Screening from EEG Signals.

A large number of studies have analyzed measurable changes that Alzheimer's disease causes on electroencephalography (EEG). Despite being easily reproducible, those markers have limited sensitivity, which reduces the interest of EEG as a screening tool for this pathology. This is for a large part due to the poor signal-to-noise ratio of EEG signals: EEG recordings are indeed usually corrupted by spurious extra-cerebral artifacts. These artifacts are responsible for a consequent degradation of the signal quality. We investigate the possibility to automatically clean a database of EEG recordings taken from patients suffering from Alzheimer's disease and healthy age-matched controls. We present here an investigation of commonly used markers of EEG artifacts: kurtosis, sample entropy, zero-crossing rate and fractal dimension. We investigate the reliability of the markers, by comparison with human labeling of sources. Our results show significant differences with the sample entropy marker. We present a strategy for semi-automatic cleaning based on blind source separation, which may improve the specificity of Alzheimer screening using EEG signals.

Quantifying statistical interdependence, part III: N > 2 point processes.

Stochastic event synchrony (SES) is a recently proposed family of similarity measures. First, "events" are extracted from the given signals; next, one tries to align events across the different time series. The better the alignment, the more similar the N time series are considered to be. The similarity measures quantify the reliability of the events (the fraction of "nonaligned" events) and the timing precision. So far, SES has been developed for pairs of one-dimensional (Part I) and multidimensional (Part II) point processes. In this letter (Part III), SES is extended from pairs of signals to N > 2 signals. The alignment and SES parameters are again determined through statistical inference, more specifically, by alternating two steps: (1) estimating the SES parameters from a given alignment and (2), with the resulting estimates, refining the alignment. The SES parameters are computed by maximum a posteriori (MAP) estimation (step 1), in analogy to the pairwise case. The alignment (step 2) is solved by linear integer programming. In order to test the robustness and reliability of the proposed N-variate SES method, it is first applied to synthetic data. We show that N-variate SES results in more reliable estimates than bivariate SES. Next N-variate SES is applied to two problems in neuroscience: to quantify the firing reliability of Morris-Lecar neurons and to detect anomalies in EEG synchrony of patients with mild cognitive impairment. Those problems were also considered in Parts I and II, respectively. In both cases, the N-variate SES approach yields a more detailed analysis.

Environment and body-brain interplay affect inhibition and decision-making.

The fine-tuned interplay of brain and body underlies human ability to cope with changes in the internal and external milieus. Previous research showed that cardiac interoceptive changes (e.g., cardiac phase) affect cognitive functions, notably inhibition that is a key element for adaptive behaviour. Here we investigated the influence on cognition of vestibular signal, which provides the brain with sensory information about body position and movement. We used a centrifuge-based design to disrupt vestibular signal in healthy human volunteers while their inhibition and decision-making functions were assessed with the stop-signal paradigm. Participants performed the standard and a novel, sensorial version of the stop-signal task to determine whether disrupted vestibular signal influences cognition as a function of its relevance to the context. First, we showed that disrupted vestibular signal was associated with a larger variability of longest inhibition latencies, meaning that participants were even slower to inhibit in the trials where they had the most difficulty inhibiting. Second, we revealed that processing of bodily information, as required in the sensorial stop-signal task, also led to a larger variability of longest inhibition latencies, which was all the more important when vestibular signal was disrupted. Lastly, we found that such a degraded response inhibition performance was due in part to the acceleration of decision-making process, meaning that participants made a decision more quickly even when strength of sensory evidence was reduced. Taken together, these novel findings provide direct evidence that vestibular signal affects the cognitive functions of inhibition and decision-making.

Probing the posture with machine learning provides physiological evidence supporting the enhanced body awareness hypothesis in trait mindfulness.

Enhanced body awareness has been suggested as one of the cognitive mechanisms that characterize mindfulness. Yet neuroscience literature still lacks strong empirical evidence to support this claim. Body awareness contributes to postural control during quiet standing; in particular, it may be argued that body awareness is more strongly engaged when standing quietly with eyes closed, because only body cues are available, than with eyes open. Under these theoretical assumptions, we recorded the postural signals of 156 healthy participants during quiet standing in Eyes closed (EC) and Eyes open (EO) conditions. In addition, each participant completed the Freiburg Mindfulness Inventory, and his/her mindfulness score was computed. Following a well-established machine learning methodology, we designed two numerical models per condition: one regression model intended to estimate the mindfulness score of each participant from his/her postural signals, and one classifier intended to assign each participant to one of the classes "Mindful" or "Non-mindful." We show that the two models designed from EC data are much more successful in their regression and classification tasks than the two models designed from EO data. We argue that these findings provide the first physiological evidence that contributes to support the enhanced body awareness hypothesis in mindfulness.

The heartbeat evoked potential does not support strong interoceptive sensibility in trait mindfulness.

The enhancement of body awareness is proposed as one of the cognitive mechanisms that characterize mindfulness. To date, this hypothesis is supported by self-report and behavioral measures but still lacks physiological evidence. The current study investigated relation between trait mindfulness (i.e., individual differences in the ability to be mindful in daily life) and body awareness in combining a self-report measure (Multidimensional Assessment of Interoceptive Awareness [MAIA] questionnaire) with analysis of the heartbeat evoked potential (HEP), which is an event-related potential reflecting the cortical processing of the heartbeat. The HEP data were collected from 17 healthy participants under five minutes of resting-state condition. In addition, each participant completed the Freiburg Mindfulness Inventory and the MAIA questionnaire. Taking account of the important variability of HEP effects, analyses were replicated with the same participants three times (in three distinct sessions). First, group-level analyses showed that HEP amplitude and trait mindfulness do not correlate. Secondly, we observed that HEP amplitude could positively correlate with self-reported body awareness; however, this association was unreliable over time. Interestingly, we found that HEP measure shows very poor reliability over time at the individual level, potentially explaining the lack of reliable association between HEP and psychological traits. Lastly, a reliable positive correlation was found between self-reported trait mindfulness and body awareness. Taken together, these findings provide preliminary evidence that the HEP might not support the increased subjective body awareness in trait mindfulness, thus suggesting that perhaps objective and subjective measures of body awareness could be independent.

Relieving peripheral neuropathic pain by increasing the power-ratio of low-ß over high-ß activities in the central cortical region with EEG-based neurofeedback: Study protocol for a controlled pilot trial (SMRPain study).

BACKGROUND: Chronic neuropathic pain associated with peripheral neuropathies cannot be attributed solely to lesions of peripheral sensory axons and likely involves alteration in the processing of nociceptive information in the central nervous system in most patients. Few data are available regarding EEG correlates of chronic neuropathic pain. The fact is that effective cortical neuromodulation strategies to treat neuropathic pain target the precentral cortical region, i.e. a cortical area corresponding to the motor cortex. It is not known how these strategies might modulate brain rhythms in the central cortical region, but it can be speculated that sensorimotor rhythms (SMRs) are modified. Another potent way of modulating cortical rhythms is to use EEG-based neurofeedback (NFB). Rare studies previously aimed at relieving neuropathic pain using EEG-NFB training. METHODS/DESIGN: The objective of this single-centre, single-blinded, randomized controlled pilot study is to assess the value of an EEG-NFB procedure to relieve chronic neuropathic pain in patients with painful peripheral neuropathy. A series of 32 patients will be randomly assigned to one of the two following EEG-NFB protocols, aimed at increasing either the low-ß(SMR)/high-ß ratio (n=16) or the α(µ)/θ ratio (n=16) at central (rolandic) cortical level. Various clinical outcome measures will be collected before and one week after 12 EEG-NFB sessions performed over 4weeks. Resting-state EEG will also be recorded immediately before and after each NFB session. The primary endpoint will be the change in the impact of pain on patient's daily functioning, as assessed on the Interference Scale of the short form of the Brief Pain Inventory. DISCUSSION: The value of EEG-NFB procedures to relieve neuropathic pain has been rarely studied. This pilot study will attempt to show the value of endogenous modulation of brain rhythms in the central (rolandic) region in the frequency band corresponding to the frequency of stimulation currently used by therapeutic motor cortex stimulation. In the case of significant clinical benefit produced by the low-ß(SMR)/high-ß ratio increasing strategy, this work could pave the way for using EEG-NFB training within the armamentarium of neuropathic pain therapy.

Toward a Refined Mindfulness Model Related to Consciousness and Based on Event-Related Potentials.

Neuroimaging, behavioral, and self-report evidence suggests that there are four main cognitive mechanisms that support mindfulness: (a) self-regulation of attention, (b) improved body awareness, (c) improved emotion regulation, and (d) change in perspective on the self. In this article, we discuss these mechanisms on the basis of the event-related potential (ERP). We reviewed the ERP literature related to mindfulness and examined a data set of 29 articles. Our findings show that the neural features of mindfulness are consistently associated with the self-regulation of attention and, in most cases, reduced reactivity to emotional stimuli and improved cognitive control. On the other hand, there appear to be no studies of body awareness. We link these electrophysiological findings to models of consciousness and introduce a unified, mechanistic mindfulness model. The main idea in this refined model is that mindfulness decreases the threshold of conscious access. We end with several working hypotheses that could direct future mindfulness research and clarify our results.

A brain-computer interface for the continuous, real-time monitoring of working memory load in real-world environments.

We developed a brain-computer interface (BCI) able to continuously monitor working memory (WM) load in real-time (considering the last 2.5 s of brain activity). The BCI is based on biomarkers derived from spectral properties of non-invasive electroencephalography (EEG), subsequently classified by a linear discriminant analysis classifier. The BCI was trained on a visual WM task, tested in a real-time visual WM task, and further validated in a real-time cross task (mental arithmetic). Throughout each trial of the cross task, subjects were given real or sham feedback about their WM load. At the end of the trial, subjects were asked whether the feedback provided was real or sham. The high rate of correct answers provided by the subjects validated not only the global behaviour of the WM-load feedback, but also its real-time dynamics. On average, subjects were able to provide a correct answer 82% of the time, with one subject having 100% accuracy. Possible cognitive and motor confounding factors were disentangled to support the claim that our EEG-based markers correspond indeed to WM.

Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals.

BACKGROUND: oscillatory activity, which can be separated in background and oscillatory burst pattern activities, is supposed to be representative of local synchronies of neural assemblies. Oscillatory burst events should consequently play a specific functional role, distinct from background EEG activity - especially for cognitive tasks (e.g. working memory tasks), binding mechanisms and perceptual dynamics (e.g. visual binding), or in clinical contexts (e.g. effects of brain disorders). However extracting oscillatory events in single trials, with a reliable and consistent method, is not a simple task. RESULTS: in this work we propose a user-friendly stand-alone toolbox, which models in a reasonable time a bump time-frequency model from the wavelet representations of a set of signals. The software is provided with a Matlab toolbox which can compute wavelet representations before calling automatically the stand-alone application. CONCLUSION: The tool is publicly available as a freeware at the address: http://www.bsp.brain.riken.jp/bumptoolbox/toolbox_home.html.

EEG paroxysmal gamma waves during Bhramari Pranayama: a yoga breathing technique.

Here we report that a specific form of yoga can generate controlled high-frequency gamma waves. For the first time, paroxysmal gamma waves (PGW) were observed in eight subjects practicing a yoga technique of breathing control called Bhramari Pranayama (BhPr). To obtain new insights into the nature of the EEG during BhPr, we analyzed EEG signals using time-frequency representations (TFR), independent component analysis (ICA), and EEG tomography (LORETA). We found that the PGW consists of high-frequency biphasic ripples. This unusual activity is discussed in relation to previous reports on yoga and meditation. It is concluded this EEG activity is most probably non-epileptic, and that applying the same methodology to other meditation recordings might yield an improved understanding of the neurocorrelates of meditation.

A cognitive brain-computer interface monitoring sustained attentional variations during a continuous task.

We introduce a cognitive brain-computer interface based on a continuous performance task for the monitoring of variations of visual sustained attention, i.e. the self-directed maintenance of cognitive focus in non-arousing conditions while possibly ignoring distractors and avoiding mind wandering. We introduce a visual sustained attention continuous performance task with three levels of task difficulty. Pairwise discrimination of these task difficulties from electroencephalographic features was performed using a leave-one-subject-out cross validation approach. Features were selected using the orthogonal forward regression supervised feature selection method. Cognitive load was best predicted using a combination of prefrontal theta power, broad spatial range gamma power, fronto-central beta power, and fronto-central alpha power. Generalization performance estimates for pairwise classification of task difficulty using these features reached 75% for 5 s epochs, and 85% for 30 s epochs.

Scale-free behaviour and metastable brain-state switching driven by human cognition, an empirical approach.

We developed a framework to study brain dynamics under cognition. In particular, we investigated the spatiotemporal properties of brain state switches under cognition. The lack of electroencephalography stationarity is exploited as one of the signatures of the metastability of brain states. We correlated power law exponents in the variables that we proposed to describe brain states, and dynamical properties of non-stationarities with cognitive conditions. This framework was successfully tested with three different datasets: a working memory dataset, an Alzheimer disease dataset, and an emotions dataset. We discuss the temporal organization of switches between states, providing evidence suggesting the need to reconsider the piecewise model, in which switches appear at discrete times. Instead, we propose a more dynamically rich view, in which besides the seemingly discrete switches, switches between neighbouring states occur all the time. These micro switches are not (physical) noise, as their properties are also affected by cognition.

EEG windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts.

EEG recordings are usually corrupted by spurious extra-cerebral artifacts, which should be rejected or cleaned up by the practitioner. Since manual screening of human EEGs is inherently error prone and might induce experimental bias, automatic artifact detection is an issue of importance. Automatic artifact detection is the best guarantee for objective and clean results. We present a new approach, based on the time-frequency shape of muscular artifacts, to achieve reliable and automatic scoring. The impact of muscular activity on the signal can be evaluated using this methodology by placing emphasis on the analysis of EEG activity. The method is used to discriminate evoked potentials from several types of recorded muscular artifacts-with a sensitivity of 98.8% and a specificity of 92.2%. Automatic cleaning of EEG data is then successfully realized using this method, combined with independent component analysis. The outcome of the automatic cleaning is then compared with the Slepian multitaper spectrum based technique introduced by Delorme et al (2007 Neuroimage 34 1443-9).

Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework.

This study addresses the problem of Alzheimer's disease (AD) diagnosis with Electroencephalography (EEG). The use of EEG as a tool for AD diagnosis has been widely studied by comparing EEG signals of AD patients only to those of healthy subjects. By contrast, we perform automated EEG diagnosis in a differential diagnosis context using a new database, acquired in clinical conditions, which contains EEG data of 169 patients: subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, possible Alzheimer's disease (AD) patients, and patients with other pathologies. We show that two EEG features, namely epoch-based entropy (a measure of signal complexity) and bump modeling (a measure of synchrony) are sufficient for efficient discrimination between these groups. We studied the performance of our methodology for the automatic discrimination of possible AD patients from SCI patients and from patients with MCI or other pathologies. A classification accuracy of 91.6% (specificity = 100%, sensitivity = 87.8%) was obtained when discriminating SCI patients from possible AD patients and 81.8% to 88.8% accuracy was obtained for the 3-class classification of SCI, possible AD and other patients.