Changes In EEG Activity Research Articles

Dynamical mesoscale model of absence seizures in genetic models.

A mesoscale network model is proposed for the development of spike and wave discharges (SWDs) in the cortico-thalamo-cortical (C-T-C) circuit. It is based on experimental findings in two genetic models of childhood absence epilepsy–rats of WAG/Rij and GAERS strains. The model is organized hierarchically into two levels (brain structures and individual neurons) and composed of compartments for representation of somatosensory cortex, reticular and ventroposteriomedial thalamic nuclei. The cortex and the two thalamic compartments contain excitatory and inhibitory connections between four populations of neurons. Two connected subnetworks both including relevant parts of a C-T-C network responsible for SWD generation are modelled: a smaller subnetwork for the focal area in which the SWD generation can take place, and a larger subnetwork for surrounding areas which can be only passively involved into SWDs, but which is mostly responsible for normal brain activity. This assumption allows modeling of both normal and SWD activity as a dynamical system (no noise is necessary), providing reproducibility of results and allowing future analysis by means of theory of dynamical system theories. The model is able to reproduce most time-frequency changes in EEG activity accompanying the transition from normal to epileptiform activity and back. Three different mechanisms of SWD initiation reported previously in experimental studies were successfully reproduced in the model. The model incorporates also a separate mechanism for the maintenance of SWDs based on coupling analysis from experimental data. Finally, the model reproduces the possibility to stop ongoing SWDs with high frequency electrical stimulation, as described in the literature.

Measuring the Domain Specificity of Workload Using EEG: Auditory and Visual Domains in Rotary-Wing Simulated Flight.

The overarching objective was to evaluate whether workload sensory-domain specificity could be identified through electroencephalogram (EEG) recordings during simulated rotary-wing operations. Rotary-wing aviators experience workload from different sensory domains, although predominantly through auditory and visual domains. Development of real-time monitoring tools using psychophysiological indices, such as EEG recordings, could enable identification of aviator overload in real time. Two studies were completed, both of which recorded EEG, task performance, and self-report data. In Study 1, 16 individuals completed a basic auditory and a basic visual laboratory task where workload was manipulated. In Study 2, 23 Army aviators completed simulated aviation flights where workload was manipulated within auditory and visual sensory domains. Results from Study 1 found differences in frontal alpha activity during the auditory task, and that alpha and beta activities were associated with perceived workload. Frontal theta activity was found to differ during the visual task while frontal alpha was associated with perceived workload. Study 2 found support for frontal beta activity and the ratio of beta to alpha + theta to differentiate level of workload within the auditory domain. There is likely a role of frontal alpha and beta activities in response to workload manipulations within the auditory domain; however, this role becomes more equivocal when examined in a multifaceted flight scenario. Results from this study provide a basis for understanding changes in EEG activity when workload is manipulated in sensory domains that can be used in furthering the development of real-time monitoring tools.

Exploration of User's Mental State Changes during Performing Brain-Computer Interface.

Substantial developments have been established in the past few years for enhancing the performance of brain–computer interface (BCI) based on steady-state visual evoked potential (SSVEP). The past SSVEP-BCI studies utilized different target frequencies with flashing stimuli in many different applications. However, it is not easy to recognize user’s mental state changes when performing the SSVEP-BCI task. What we could observe was the increasing EEG power of the target frequency from the user’s visual area. BCI user’s cognitive state changes, especially in mental focus state or lost-in-thought state, will affect the BCI performance in sustained usage of SSVEP. Therefore, how to differentiate BCI users’ physiological state through exploring their neural activities changes while performing SSVEP is a key technology for enhancing the BCI performance. In this study, we designed a new BCI experiment which combined working memory task into the flashing targets of SSVEP task using 12 Hz or 30 Hz frequencies. Through exploring the EEG activity changes corresponding to the working memory and SSVEP task performance, we can recognize if the user’s cognitive state is in mental focus or lost-in-thought. Experiment results show that the delta (1–4 Hz), theta (4–7 Hz), and beta (13–30 Hz) EEG activities increased more in mental focus than in lost-in-thought state at the frontal lobe. In addition, the powers of the delta (1–4 Hz), alpha (8–12 Hz), and beta (13–30 Hz) bands increased more in mental focus in comparison with the lost-in-thought state at the occipital lobe. In addition, the average classification performance across subjects for the KNN and the Bayesian network classifiers were observed as 77% to 80%. These results show how mental state changes affect the performance of BCI users. In this work, we developed a new scenario to recognize the user’s cognitive state during performing BCI tasks. These findings can be used as the novel neural markers in future BCI developments.

Time delay of the qCON monitor and its performance during state transitions

We investigated the performance of the qCON index regarding its time delay for sudden changes in the anesthetic level as well as to separate responsiveness from unresponsiveness during loss and return of responsiveness (LOR and ROR). For evaluation of the time delay, we replayed relevant EEG episodes to the qCON to simulate sudden changes between the states (i) awake/sedation, (ii) adequate anesthesia, or (iii) suppression. We also replayed EEG from 40 patients during LOR and ROR to evaluate the qCON’s ability to separate responsiveness from unresponsiveness. The time delays depended on the type of transition. The delays for the important transition between awake/sedation and adequate anesthesia were 21(5) s from awake/sedation to adequate anesthesia and 26(5) s in the other direction. The performance of the qCON to separate responsiveness from unresponsiveness depended on signal quality, the investigation window, i.e. ± 30 s or ± 60 s around LOR/ROR, and the specific transition being tested. AUC was 0.63–0.90 for LOR and 0.61–0.79 for ROR. Time delay and performance during state transitions of the qCON were similar to other monitoring systems such as bispectral index. The better performance of qCON during LOR than ROR probably reflects the sudden change in EEG activity during LOR and the more heterogeneous EEG during ROR.

Cortical Oscillations during Gait: Wouldn't Walking be so Automatic?

Gait is often considered as an automatic movement but cortical control seems necessary to adapt gait pattern with environmental constraints. In order to study cortical activity during real locomotion, electroencephalography (EEG) appears to be particularly appropriate. It is now possible to record changes in cortical neural synchronization/desynchronization during gait. Studying gait initiation is also of particular interest because it implies motor and cognitive cortical control to adequately perform a step. Time-frequency analysis enables to study induced changes in EEG activity in different frequency bands. Such analysis reflects cortical activity implied in stabilized gait control but also in more challenging tasks (obstacle crossing, changes in speed, dual tasks…). These spectral patterns are directly influenced by the walking context but, when analyzing gait with a more demanding attentional task, cortical areas other than the sensorimotor cortex (prefrontal, posterior parietal cortex, etc.) seem specifically implied. While the muscular activity of legs and cortical activity are coupled, the precise role of the motor cortex to control the level of muscular contraction according to the gait task remains debated. The decoding of this brain activity is a necessary step to build valid brain–computer interfaces able to generate gait artificially.

Nonlinear Transfer Entropy to Assess the Neurovascular Coupling in Premature Neonates.

In the adult brain, it is well known that increases in local neural activity trigger changes in regional blood flow and, thus, changes in cerebral energy metabolism. This regulation mechanism is called neurovascular coupling (NVC). It is not yet clear to what extent this mechanism is present in the premature brain. In this study, we explore the use of transfer entropy (TE) in order to compute the nonlinear coupling between changes in brain function, assessed by means of EEG, and changes in brain oxygenation, assessed by means of near-infrared spectroscopy (NIRS). In a previous study, we measured the coupling between both variables using a linear model to compute TE. The results indicated that changes in brain oxygenation were likely to precede changes in EEG activity. However, using a nonlinear and nonparametric approach to compute TE, the results indicate an opposite directionality of this coupling. The source of the different results provided by the linear and nonlinear TE is unclear and needs further research. In this study, we present the results from a cohort of 21 premature neonates. Results indicate that TE values computed using the nonlinear approach are able to discriminate between neonates with brain abnormalities and healthy neonates, indicating a less functional NVC in neonates with brain abnormalities.

IS IT WORTH EXAMINING EEG FUNCTION IN PEOPLE UNDERGOING ORTHOPAEDIC SURGERY?

The vast majority of hip or knee arthroplasty finishes with improved limb functional status. Despite this, some patients do not recover; they "save" the operated limb; they cannot trust it. This also happens in patients whose range of motion and muscle strength are rated as very good. It is possible, therefore, that the causes must be located in the movement initiating centre, in the motor cortex. We can link the activation and changes in the metabolism of the cerebral cortex, e.g. during limb movement, with the changes in bioelectrical activity visible in electroencephalography (EEG). Conclusions: Our literature analysis shows that so far, no study has been conducted to check whether EEG activity changes over the sensory-motor region after lower limb arthroplasty. We propose the design of such a study with an analysis of potential difficulties that would have to be overcome by the team undertaking the challenge. The obtained data could be the basis for neurorehabilitation using the EEG biofeedback method. Effective interventions would further improve the quality of life.

Distinguishing mental attention states of humans via an EEG-based passive BCI using machine learning methods

Recent advances in technology bring about novel operating environments where the role of human participants is reduced to passive observation. While opening new frontiers in productivity and lifestyle, such environments also create hazards related to the inability of human individuals to maintain focus and concentration during passive control tasks. A passive brain-computer interface for monitoring mental attention states of human individuals (focused, unfocused, and drowsy) by using electroencephalographic (EEG) brain activity imaging and machine learning data analysis methods is developed in this work. An EEG data processing pipeline and a machine learning mental state detection algorithm using the Support Vector Machine (SVM) method were designed and compared with k-Nearest Neighbor and Adaptive Neuro-Fuzzy System methods. To collect 25 h of EEG data from 5 participants, a classic EEG headset was modified. We found that the changes in EEG activity in frontal and parietal lobes occurring at 1–5 Hz and 10–15 Hz frequency bands were associated with the changes in individuals’ attention state. We demonstrated the ability to use such changes to identify individuals’ attention state with 96.70% (best) and 91.72% (avg.) accuracy in experimental settings using a version of continuous performance task with SVM-based mental state detector. The findings help guide the design of future systems for monitoring the state of human individuals by means of EEG brain activity data.

Changes in Theta but not Alpha Modulation Are Associated with Impairment in Working Memory in Alzheimer's Disease and Mild Cognitive Impairment.

While several studies have found that neural oscillations play a key role in the functioning of working memory, the nature of aberrant oscillatory activity underlying working memory impairments in Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains largely unexplored. These individuals often display structural alterations in brain regions and pathways involved in working memory processes and therefore may also display altered oscillatory activity during memory activation. Electroencephalographic (EEG) activity was recorded during the N-back working memory task in three groups: AD (n = 29), MCI (n = 100), and healthy controls (HCs; n = 40). Theta (4-7 Hz) and alpha (7.5-12 Hz) modulation was measured in response to the stimulus presentation during correct and incorrect responses. This modulation represents the change in EEG activity associated with the stimulus onset and was measured as a ratio of post stimulus power to pre stimulus power. We also assessed the relationship between change in oscillatory power and working memory performance. Compared to HCs, the AD group demonstrated the lowest working memory accuracy and a smaller theta ratio for correct responses on the 2-back condition; the MCI group demonstrated a smaller theta ratio for correct responses on the 3-back condition. Finally, we observed that the theta ratio, but not the alpha ratio, was a significant predictor of working memory performance in the three groups for all conditions. Taken together, these behavioral and electrophysiological results suggest that in addition to impairments in working memory performance, modulation of theta, but not alpha power, may be impaired in MCI and AD.

Dynamics of brain activation during learning of syllable-symbol paired associations

Initial stages of reading acquisition require the learning of letter and speech sound combinations. While the long-term effects of audio-visual learning are rather well studied, relatively little is known about the short-term learning effects at the brain level. Here we examined the cortical dynamics of short-term learning using magnetoencephalography (MEG) and electroencephalography (EEG) in two experiments that respectively addressed active and passive learning of the association between shown symbols and heard syllables. In experiment 1, learning was based on feedback provided after each trial. The learning of the audio-visual associations was contrasted with items for which the feedback was meaningless. In experiment 2, learning was based on statistical learning through passive exposure to audio-visual stimuli that were consistently presented with each other and contrasted with audio-visual stimuli that were randomly paired with each other. After 5–10 min of training and exposure, learning-related changes emerged in neural activation around 200 and 350 ms in the two experiments. The MEG results showed activity changes at 350 ms in caudal middle frontal cortex and posterior superior temporal sulcus, and at 500 ms in temporo-occipital cortex. Changes in brain activity coincided with a decrease in reaction times and an increase in accuracy scores. Changes in EEG activity were observed starting at the auditory P2 response followed by later changes after 300 ms. The results show that the short-term learning effects emerge rapidly (manifesting in later stages of audio-visual integration processes) and that these effects are modulated by selective attention processes.

Changes in background electroencephalographic activity in benign childhood epilepsy with centrotemporal spikes after oxcarbazepine treatment: a standardized low-resolution brain electromagnetic tomography (sLORETA) study

BackgroundSeveral neuroimaging studies have reported neurophysiological alterations in patients with benign childhood epilepsy with centrotemporal spikes (BCECTS). However, reported outcomes have been inconsistent, and the progression of these changes in the brain remains unresolved. Moreover, background electroencephalography (EEG) in cases of BCECTS has not been performed often.MethodsWe investigated background EEG activity changes after six months of oxcarbazepine treatment to better understand the neurophysiological alterations and progression that occur in BCECTS. In 18 children with BCECTS, non-parametric statistical analyses using standardized low resolution brain electromagnetic tomography (sLORETA) were performed to compare the current density distribution of four frequency bands (delta, theta, alpha, and beta) between untreated and treated conditions.ResultsBackground EEG activity for the delta frequency band was significantly decreased in the fronto-temporal and limbic regions of the left hemisphere after oxcarbazepine treatment (threshold log-F-ratio = ±2.729, P < 0.01). The maximum current density difference was found in the parahippocampal gyrus of the left limbic lobe (Montreal Neurological Institute coordinate [x, y, z = 25, − 20, − 10], Brodmann area 28) (log-F-ratio = 3.081, P < 0.01).ConclusionsOur results indicate the involvement of the fronto-temporal and limbic cortices in BCECTS, and limbic lobe involvement, including the parahippocampal gyrus, was noted. In addition to evidence of the involvement of the fronto-temporal and limbic cortices in BCECTS, this study also found that an antiepileptic drug could reduce the delta frequency activity of the background EEG in these regions.

Effects of Ageing and Sex on Complexity in the Human Sleep EEG: A Comparison of Three Symbolic Dynamic Analysis Methods

Symbolic dynamic analysis (SDA) methods have been applied to biomedical signals and have been proven efficient in characterising differences in the electroencephalogram (EEG) in various conditions (e.g., epilepsy, Alzheimer’s, and Parkinson’s diseases). In this study, we investigated the use of SDA on EEGs recorded during sleep. Lempel‐Ziv complexity (LZC), permutation entropy (PE), and permutation Lempel‐Ziv complexity (PLZC), as well as power spectral analysis based on the fast Fourier transform (FFT), were applied to 8‐h sleep EEG recordings in healthy men (n=31) and women (n=29), aged 20‐74 years. The results of the SDA methods and FFT analysis were compared and the effects of age and sex were investigated. Surrogate data were used to determine whether the findings with SDA methods truly reflected changes in nonlinear dynamics of the EEG and not merely changes in the power spectrum. The surrogate data analysis showed that LZC merely reflected spectral changes in EEG activity, whereas PE and PLZC reflected genuine changes in the nonlinear dynamics of the EEG. All three SDA techniques distinguished the vigilance states (i.e., wakefulness, REM sleep, NREM sleep, and its sub‐stages: stage 1, stage 2, and slow wave sleep). Complexity of the sleep EEG increased with ageing. Sex on the other hand did not affect the complexity values assessed with any of these three SDA methods, even though FFT detected sex differences. This study shows that SDA provides additional insights into the dynamics of sleep EEG and how it is affected by ageing.

Do Complexity Measures of Frontal EEG Distinguish Loss of Consciousness in Geriatric Patients Under Anesthesia?

While geriatric patients have a high likelihood of requiring anesthesia, they carry an increased risk for adverse cognitive outcomes from its use. Previous work suggests this could be mitigated by better intraoperative monitoring using indexes defined by several processed electroencephalogram (EEG) measures. Unfortunately, inconsistencies between patients and anesthetic agents in current analysis techniques have limited the adoption of EEG as standard of care. In attempts to identify new analyses that discriminate clinically-relevant anesthesia timepoints, we tested 1/f frequency scaling as well as measures of complexity from nonlinear dynamics. Specifically, we tested whether analyses that characterize time-delayed embeddings, correlation dimension (CD), phase-space geometric analysis, and multiscale entropy (MSE) capture loss-of-consciousness changes in EEG activity. We performed these analyses on EEG activity collected from a traditionally hard-to-monitor patient population: geriatric patients on beta-adrenergic blockade who were anesthetized using a combination of fentanyl and propofol. We compared these analyses to traditional frequency-derived measures to test how well they discriminated EEG states before and after loss of response to verbal stimuli. We found spectral changes similar to those reported previously during loss of response. We also found significant changes in 1/f frequency scaling. Additionally, we found that our phase-space geometric characterization of time-delayed embeddings showed significant differences before and after loss of response, as did measures of MSE. Our results suggest that our new spectral and complexity measures are capable of capturing subtle differences in EEG activity with anesthesia administration—differences which future work may reveal to improve geriatric patient monitoring.

EMG biofeedback training in adult attention-deficit/hyperactivity disorder: An active (control) training?

ObjectiveThe present study aimed at revealing neurophysiological effects induced by electromyography (EMG) based biofeedback, considered as a semi-active control condition in neurofeedback studies, in adult attention-deficit/hyperactivity disorder (ADHD) patients. Methods20 adult ADHD patients trained their muscle activity in the left and right supraspinatus muscle over the course of 30 EMG biofeedback sessions. Changes induced by the EMG feedback were evaluated at a clinical and neurophysiological level; additionally, the relation between changes in EEG activity recorded at the vertex over the training course and changes of symptom severity over the treatment course were assessed in order to investigate the mechanisms underlying clinical effects of EMG biofeedback. ResultsParticipants showed significant behavioral improvements on a self-rating scale. There was a significant increase in alpha power, but no significant changes in the delta frequency range; changes in the theta and beta frequency range were not significant after adjustment for multiple comparisons. No statistically significant correlation was found between changes in EEG frequency bands and changes in ADHD symptoms. ConclusionsThe current results assessed by means of a single-electrode EEG constitute a starting point regarding a clearer understanding of mechanisms underlying clinical effects of EMG biofeedback. Although we did not reveal systematic effects induced by EMG feedback on brain activity it remains an open question whether EMG biofeedback induces changes in brain regions or parameters we did not gather in the present study (e.g. motor cortex).

Reevaluation of CNS Oxygen Toxicity Seizures in Male Sprague‐Dawley Rats

Prolonged breathing of hyperbaric oxygen (HBO2) results in central nervous system O2 toxicity (CNS‐OT; i.e., seizures, Sz's). The threat of CNS‐OT is what limits the use of HBO2 in hyperbaric, diving and submarine medicine. Past studies have identified CNS‐OT as the onset of visibly detectable tonic‐clonic Sz's preceded by increased cortical EEG activity. It has been our experience, however, that onset of 1st Sz in Sprague‐Dawley (SD) rats is variable between animals and, in some cases, difficult to confirm in real time. This is problematic because the latency time to seizures (LSz) is how the risk of CNS‐OT is evaluated in hyperbaric research. Consequently, we have begun using longer exposures to HBO2 to capture recurring changes in EEG activity and behaviors that can be analyzed offline to redefine the criteria for 1st Sz. We also wanted to know if subsequent Sz's increased in intensity. Accordingly, cortical EEGs were recorded in 27 adult male SD rats that were implanted with a DSI 4‐ET radio transmitter. At least 1 week following surgery, each rat was placed in a hyperbaric chamber and “dived” to 5 ATA O2. 71 Sz's were identified in 27 rats based on quantified changes in cortical EEG activity (NeuroScore 2.1) and our modified Racine scale of six Sz‐related behaviors. A cortical seizure (CSz) was defined as a series of EEG polyspikes that crossed a threshold of 200μV, lasted ≥15 seconds, and contained an increase in theta wave activity coupled with at least one of the Sz‐related behaviors (65% of the Sz were classified as CSz). Two other Sz types were also recorded: neurological seizures (NSz) that included only the EEG profile described above, but without any detectable behavior changes (14%); and behavioral seizure (BSz) that included one or more of the behavioral components without any detectable changes in cortical EEG activity (21%). LSz was calculated from the moment the chamber pressure reached 5 ATA O2 until the onset of CSz, NSz or BSz. The LSz to 1st seizure was 7.2 ± 5.3 min (n= 27), while the 2nd and 3rd Sz's occurred at 26.2 ± 29.88 min (n=25) and 40 ± 40.86 min (n=13), respectively. Remarkably, in between bouts of Sz's, animals exhibited normal behaviors (grooming, exploring, chewing, etc.) and normal EEG activity in 5 ATA O2. There was also a shift in the type of Sz between the 1st and 2nd Sz such that the fraction of BSz + NSz significantly increased from 22% to 44%. Moreover, intensity of the Sz increased progressively with each successive Sz based on our modified Racine score. We conclude that the majority of HBO2 Sz's in SD rats can be readily identified using a combination of defined EEG and behavioral criteria. However, not all Sz's during HBO2 fit both criteria. The occurrence of BSz suggests that neurological Sz's originate outside the cerebral cortex. The occurrence of NSz indicate that some Sz's will be missed in studies that do not measure EEG activity. The fact that 1st Sz is often underwhelming compared to subsequent Sz's suggests the LSz may be overestimated in some animals. To better understand the SD rat model of CNS‐OT, we recommend that future studies need to 1) implant telemetric leads into multiple cortical and subcortical areas to localize the site of Sz‐genesis; and 2) determine the degree of performance impairment during 1st, 2nd &amp; 3rd Sz's.Support or Funding InformationONR N000141310405 &amp; N000141612537

Repetitive transcranial magnetic stimulation modulates the impact of a negative mood induction.

High frequency repetitive Transcranial Magnetic Stimulation (rTMS) over the left dorsolateral prefrontal cortex (DLPFC) has been found to alleviate depressive symptoms. However, the mechanisms driving these effects are still poorly understood. In the current study, we tested the idea that this intervention protects against negative mood shifts following emotional provocation. We furthermore explored changes in EEG activity (frontal alpha asymmetry) and effects on attentional processing (emotional Stroop). To this end, 23 healthy individuals participated in two sessions separated by one week, whereby they once received 15 min of 10Hz rTMS stimulation (1500 pulses) at 110% of the individual motor threshold, and once sham stimulation. Then, negative mood was induced using sad movie clips. The results revealed a significantly stronger mood decline following rTMS compared to sham stimulation. No changes were observed in frontal alpha asymmetry and attentional processing. Our findings are at odds with the view that high frequency rTMS over the left DLPFC directly protects against the induction of negative mood, but rather suggest that it enhances the effects of emotional provocation. Possibly, in healthy young individuals, this stimulation protocol heightens susceptibility to mood induction procedures in general.

Treatment Effects on Neonatal EEG.

Conventional EEG and amplitude-integrated electroencephalography are used in neonates to assess prognosis and significant changes in brain activity. Neuroactive medications and hypothermia can influence brain activity and therefore alter EEG interpretation. There are limited studies on the effect of these therapies on neonatal EEG background activity. Medication effects on the EEG or amplitude-integrated electroencephalography include increased interburst interval duration, voltage suppression, and sleep disruption. The effect is transient in term newborns but can be persistent in premature newborns. Although therapeutic hypothermia does not produce significant changes in EEG activity, it does change the time point at which EEG can accurately predict neurodevelopmental outcome. It is important to account for these effects on the EEG to avoid inaccurate interpretation that may affect prognostication.

Expectation of nocebo hyperalgesia affects EEG alpha-activity

Changes in EEG activity have been related to clinical and experimental pain. Expectation of a negative outcome can lead to pain enhancement (nocebo hyperalgesia) and can alter the response to therapeutic interventions. The present study characterizes EEG alteration related to pain facilitation by nocebo. Thirty healthy subjects were randomly assigned to the nocebo or control group. Five-minute EEG was recorded under: resting state, tonic innocuous heat and tonic noxious heat before and after the application of a sham inert cream to the non-dominant volar forearm combined with cognitive manipulation. The intensity and unpleasantness of heat-induced pain increased after cognitive manipulation in the nocebo group compared to control and was associated with enhanced low alpha (8–10Hz) activity. However, changes in alpha activity were predicted by catastrophizing but not by pain intensity or unpleasantness, which suggest that low alpha power might reflect brain activity related to negative cognitive–affective responses to pain.

The Do's and Don'ts of Neurofeedback Training: A Review of the Controlled Studies Using Healthy Adults.

The goal of EEG neurofeedback (EEG-NFB) training is to induce changes in the power of targeted EEG bands to produce beneficial changes in cognitive or motor function. The effectiveness of different EEG-NFB protocols can be measured using two dependent variables: (1) changes in EEG activity and (2) behavioral changes of a targeted function (for therapeutic applications the desired changes should be long-lasting). To firmly establish a causal link between these variables and the selected protocol, similar changes should not be observed when appropriate control paradigms are used. The main objective of this review is to evaluate the evidence, reported in the scientific literature, which supports the validity of various EEG-NFB protocols. Our primary concern is to highlight the role that uncontrolled nonspecific factors can play in the results generated from EEG-NFB studies. Nonspecific factors are often ignored in EEG-NFB designs or the data are not presented, which means conclusions should be interpreted cautiously. As an outcome of this review we present a do's and don'ts list, which can be used to develop future EEG-NFB methodologies, based on the small set of experiments in which the proper control groups have excluded non-EEG-NFB related effects. We found two features which positively correlated with the expected changes in power of the trained EEG band(s): (1) protocols which focused on training a smaller number of frequency bands and (2) a bigger number of electrodes used for neurofeedback training. However, we did not find evidence in support of the positive relationship between power changes of a trained frequency band(s) and specific behavioral effects.

Modeling the effect of sleep regulation on a neural mass model.

In mammals, sleep is categorized by two main sleep stages, rapid eye movement (REM) and non-REM (NREM) sleep that are known to fulfill different functional roles, the most notable being the consolidation of memory. While REM sleep is characterized by brain activity similar to wakefulness, the EEG activity changes drastically with the emergence of K-complexes, sleep spindles and slow oscillations during NREM sleep. These changes are regulated by circadian and ultradian rhythms, which emerge from an intricate interplay between multiple neuronal populations in the brainstem, forebrain and hypothalamus and the resulting varying levels of neuromodulators. Recently, there has been progress in the understanding of those rhythms both from a physiological as well as theoretical perspective. However, how these neuromodulators affect the generation of the different EEG patterns and their temporal dynamics is poorly understood. Here, we build upon previous work on a neural mass model of the sleeping cortex and investigate the effect of those neuromodulators on the dynamics of the cortex and the corresponding transition between wakefulness and the different sleep stages. We show that our simplified model is sufficient to generate the essential features of human EEG over a full day. This approach builds a bridge between sleep regulatory networks and EEG generating neural mass models and provides a valuable tool for model validation.