Brain Rhythms Research Articles

Functional connectivity and cognitive decline: a review of rs-fMRI, EEG, MEG, and graph theory approaches in aging and dementia

Age-related changes in the brain cause cognitive decline and dementia. In recent year’s researchers’ extensively studied the relationship between age related changes in functional connectivity (FC) in dementia. Those studies explore the alterations in FC patterns observed in aging and neurodegenerative disorders using techniques such as resting-state functional magnetic resonance imaging (rs-fMRI), electroencephalography (EEG) coherence analysis, and graph theory approaches. The current review summarizes the findings, which highlight the impact of FC changes on cognitive decline and neurodegenerative disease progression using these techniques and emphasize the importance of understanding neural alterations for early detection and intervention. The findings underscore the complexity of cognitive aging and the need for further research to differentiate normal aging from pathological conditions. rs-fMRI is essential for studying brain changes associated with aging and pathology by capturing coherent fluctuations in brain activity during rest, providing insights into FC without task-related confounds. Key networks such as the default mode network and front parietal control network are crucial in revealing age-related connectivity changes. Despite challenges like neurovascular uncoupling and data complexity, ongoing advancements promise improved clinical applications of rs-fMRI in understanding cognitive decline across the lifespan. EEG and magnetoencephalography (MEG) are cost-effective techniques with high temporal resolution, allowing detailed study of brain rhythms and FC. Recent studies highlight EEG/MEG’s potential in early Alzheimer’s disease detection by identifying changes in brain connectivity patterns. Integration of machine learning techniques enhances diagnostic accuracy, although further validation and research are necessary. Graph theory offers a quantitative framework to analyze cognitive networks, identifying distinct topological differences between healthy aging and pathological conditions. Future research should expand exploration into diverse neurodegenerative disorders beyond mild cognitive impairment, integrating neuroimaging techniques to refine diagnostic precision and deepen insights into brain function and connectivity.

Cell-type specific contributions to theta-gamma coupled rhythms in the hippocampus

Abstract Distinct inhibitory cell types participate in cognitively relevant nested brain rhythms, and particular changes in such rhythms are known to occur in disease states. Specifically, the co-expression of theta and gamma rhythms in the hippocampus is believed to represent a general coding scheme, but cellular-based generation mechanisms for these coupled rhythms are currently unclear. We develop a population rate model of the CA1 hippocampus that encompasses circuits of three inhibitory cell types (bistratified cells, parvalbumin (PV)-expressing and cholecystokinin (CCK)-expressing basket cells) and pyramidal cells to examine this. We constrain parameters and perform numerical and theoretical analyses. The theory, in combination with the numerical explorations, predicts circuit motifs and specific cell-type mechanisms that are essential for the co-existence of theta and gamma oscillations. We find that CCK-expressing basket cells initiate the coupled rhythms and regularize theta, and PV-expressing basket cells enhance both theta and gamma rhythms. Pyramidal and bistratified cells govern the generation of theta rhythms, and PV-expressing basket and pyramidal cells play dominant roles in controlling theta frequencies. Our circuit motifs for theta-gamma coupled rhythm generation could be applicable to other brain regions.

Central pattern generator control of a vertebrate ultradian sleep rhythm.

The mechanisms underlying the mammalian ultradian sleep rhythm-the alternation of rapid-eye-movement (REM) and slow-wave (SW) states-are not well understood but probably depend, at least in part, on circuits in the brainstem1-6. Here, we use perturbation experiments to probe this ultradian rhythm in sleeping lizards (Pogona vitticeps)7-9 and test the hypothesis that it originates in a central pattern generator10,11-circuits that are typically susceptible to phase-dependent reset and entrainment by external stimuli12. Using light pulses, we find that Pogona's ultradian rhythm8 can be reset in a phase-dependent manner, with a critical transition from phase delay to phase advance in the middle of SW. The ultradian rhythm frequency can be decreased or increased, within limits, by entrainment with light pulses. During entrainment, Pogona REM (REMP)can be shortened but not lengthened, whereas SW can be dilated more flexibly. In awake animals, a few alternating light/dark epochs matching natural REMP and SW durations entrain a sleep-like brain rhythm, suggesting the transient activation of an ultradian rhythm generator. In sleeping animals, a light pulse delivered to a single eye causes an immediate ultradian rhythm reset, but only of the contralateral hemisphere; both sides resynchronize spontaneously, indicating that sleep is controlled by paired rhythm-generating circuits linked by functional excitation. Our results indicate that central pattern generators of a type usually known to control motor rhythms may also organize the ultradian sleep rhythm in a vertebrate.

Classification of EEG evoked in 2D and 3D virtual reality: traditional machine learning versus deep learning

Backgrounds. Virtual reality (VR) simulates real-life events and scenarios and is widely utilized in education, entertainment, and medicine. VR can be presented in two dimensions (2D) or three dimensions (3D), with 3D VR offering a more realistic and immersive experience. Previous research has shown that electroencephalogram (EEG) profiles induced by 3D VR differ from those of 2D VR in various aspects, including brain rhythm power, activation, and functional connectivity. However, studies focused on classifying EEG in 2D and 3D VR contexts remain limited.Methods. A 56-channel EEG was recorded while visual stimuli were presented in 2D and 3D VR. The recorded EEG signals were classified using two machine learning approaches: traditional machine learning and deep learning. In the traditional approach, features such as power spectral density (PSD) and common spatial patterns (CSP) were extracted, and three classifiers-support vector machines (SVM), K-nearest neighbors (KNN), and random forests (RF)-were used. For the deep learning approach, a specialized convolutional neural network, EEGNet, was employed. The classification performance of these methods was then compared.Results. In terms of accuracy, precision, recall, and F1-score, the deep learning method outperformed traditional machine learning approaches. Specifically, the classification accuracy using the EEGNet deep learning model reached up to 97.86%.Conclusions. EEGNet-based deep learning significantly outperforms conventional machine learning methods in classifying EEG signals induced by 2D and 3D VR. Given EEGNet's design for EEG-based brain-computer interfaces (BCI), this superior classification performance suggests that it can enhance the application of 3D VR in BCI systems.

A Proof-of-Concept Open-Source Platform for Neural Signal Modulation and Its Applications in IoT and Cyber-Physical Systems

This paper presents the design, implementation, and characterization of a digital IoT platform capable of generating brain rhythm frequencies using synchronous digital logic. Designed with the Google SkyWater 130 nm open-source process design kit (PDK), this platform emulates Alpha, Beta, and Gamma rhythms. As a proof of concept and the first of its kind, this device showcases its potential applications in both industrial and academic settings. The platform was integrated with an IoT device to optimize and accelerate research and development efforts in embedded systems. Its cost-effective and efficient performance opens opportunities for real-time neural signal processing and integrated healthcare. The presented digital platform serves as a valuable educational tool, enabling researchers to engage in hands-on learning and experimentation with IoT technologies and system-level hardware–software integration at the device level. By utilizing open-source tools, this research demonstrates a cost-effective approach, fostering innovation and bridging the gap between theoretical knowledge and practical application. Furthermore, the proposed system-level design can be interfaced with various serial devices, Wi-Fi modules, ARM processors, and mobile applications, illustrating its versatility and potential for future integration into broader IoT ecosystems. This approach underscores the value of open-source solutions in driving technological advancements and addressing skills shortages.

Age-related distinctions in cooperative dynamics of brain rhythms during sleep-wake transitions

Age-related distinctions in cooperative dynamics of brain rhythms during sleep-wake transitions

Threshold crossing time theory for quasicycles with application to brain rhythms

Threshold crossing time theory for quasicycles with application to brain rhythms

Optogenetic targeting of cortical astrocytes selectively improves NREM sleep in an Alzheimer’s disease mouse model

Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by memory impairments and distinct histopathological features such as amyloid-beta (Aβ) accumulations. Alzheimer’s patients experience sleep disturbances at early stages of the disease. APPswe/PS1dE9 (APP) mice exhibit sleep disruptions, including reductions in non-rapid eye movement (NREM) sleep, that contribute to their disease progression. In addition, astrocytic calcium transients associated with a sleep-dependent brain rhythm, slow oscillations prevalent during NREM sleep, are disrupted in APP mice. However, at present it is unclear whether restoration of circuit function by targeting astrocytic activity could improve sleep in APP mice. To that end, APP mice expressing channelrhodopsin-2 (ChR2) targeted to astrocytes underwent optogenetic stimulation at the slow oscillation frequency. Optogenetic stimulation of astrocytes significantly increased NREM sleep duration but not duration of rapid eye movement (REM) sleep. Optogenetic treatment increased delta power and reduced sleep fragmentation in APP mice. Thus, optogenetic activation of astrocytes increased sleep quantity and improved sleep quality in an AD mouse model. Astrocytic activity provides a novel therapeutic avenue to pursue for enhancing sleep and slowing AD progression.

Aperiodic EEG Predicts Variability of Visual Temporal Processing.

The human brain exhibits both oscillatory and aperiodic, or 1/f, activity. Although a large body of research has focused on the relationship between brain rhythms and sensory processes, aperiodic activity has often been overlooked as functionally irrelevant. Prompted by recent findings linking aperiodic activity to the balance between neural excitation and inhibition, we investigated its effects on the temporal resolution of perception. We recorded electroencephalography (EEG) from participants (both sexes) during the resting state and a task in which they detected the presence of two flashes separated by variable interstimulus intervals. Two-flash discrimination accuracy typically follows a sigmoid function whose steepness reflects perceptual variability or inconsistent integration/segregation of the stimuli. We found that individual differences in the steepness of the psychometric function correlated with EEG aperiodic exponents over posterior scalp sites. In other words, participants with flatter EEG spectra (i.e., greater neural excitation) exhibited increased sensory noise, resulting in shallower psychometric curves. Our finding suggests that aperiodic EEG is linked to sensory integration processes usually attributed to the rhythmic inhibition of neural oscillations. Overall, this correspondence between aperiodic neural excitation and behavioral measures of sensory noise provides a more comprehensive explanation of the relationship between brain activity and sensory integration and represents an important extension to theories of how the brain samples sensory input over time.

Differences in Mu rhythm when seeing grasping/motor actions in a real context versus on screens

Mu rhythm (∼8–12 Hz) in the somatosensory cortex has traditionally been linked with doing and seeing motor activities. Here, we aimed to learn how the medium (physical or screened) in which motor actions are seen could impact on that specific brain rhythm. To do so, we presented to 40 participants the very same narrative content both in a one-shot movie with no cuts and in a real theatrical performance. We recorded subjects’ brain activities with electroencephalographic (EEG) procedures, and analyzed Mu rhythm present in left (C3) and right (C4) somatosensory areas in relation to the 24 motor activities included in each visual stimulus (screen vs. reality) (24 motor and grasping actions x 40 participants x 2 conditions = 1920 trials). We found lower Mu spectral power in the somatosensory area after the onset of the motor actions in real performance than on-screened content, more pronounced in the left hemisphere. In our results, the sensorimotor Mu-ERD (event-related desynchronization) was stronger during the real-world observation compared to screen observation. This could be relevant in research areas where the somatosensory cortex is important, such as online learning, virtual reality, or brain-computer interfaces.

Systemic neurophysiological entrainment to behaviorally relevant rhythmic stimuli.

Physiological oscillations, such as those involved in brain activity, heartbeat, and respiration, display inherent rhythmicity across various timescales. However, adaptive behavior arises from the interaction between these intrinsic rhythms and external environmental cues. In this study, we used multimodal neurophysiological recordings, simultaneously capturing signals from the central and autonomic nervous systems (CNS and ANS), to explore the dynamics of brain and body rhythms in response to rhythmic auditory stimulation across three conditions: baseline (no auditory stimulation), passive auditory processing, and active auditory processing (discrimination task). Our findings demonstrate that active engagement with auditory stimulation synchronizes both CNS and ANS rhythms with the external rhythm, unlike passive and baseline conditions, as evidenced by power spectral density (PSD) and coherence analyses. Importantly, phase angle analysis revealed a consistent alignment across participants between their physiological oscillatory phases at stimulus or response onsets. This alignment was associated with reaction times, suggesting that certain phases of physiological oscillations are spontaneously prioritized across individuals due to their adaptive role in sensorimotor behavior. These results highlight the intricate interplay between CNS and ANS rhythms in optimizing sensorimotor responses to environmental demands, suggesting a potential mechanism of embodied predictive processing.

Linear Monotonic Inter-electrode Associations as Quantitative EEG for Alcoholism Diagnosis

Alcohol use disorders (AUDs) are associated with alterations in EEG patterns that reflect underlying neural dysfunctions, such as impaired cognitive processing, reduced neural connectivity, and disrupted brain rhythms. These EEG alterations can be indicative of several brain disorders, including cognitive deficits, mood disorders, and neurodegenerative diseases, which are often exacerbated by chronic alcohol abuse. Cognitive impairment in alcoholic subjects is caused due to altered functional connectivity between brain regions that can be quantified using statistical measures such as correlation. This paper proposes novel Quantitative EEG (QEEG) features comprising the band-wise absolute value of inter-electrode correlations as a measure of brain functional connectivity to classify alcoholic and non-alcoholic EEG. The EEG signal is first decomposed into five frequency sub-bands. In each sub-band, the absolute value of the linear Pearson product-moment correlation coefficient is computed between time-series EEG recorded by electrode pairs placed over different brain regions. To reduce the dimensionality of the feature vector, an ensemble feature selection approach is adopted, utilizing ANOVA and Chi-square, in conjunction with greedy forward feature selection wrapper algorithm. Four classifiers, namely SVM, KNN, ANN and RF are utilized for the classification. Among them, the SVM classifier achieves the best classification accuracies of 100% on validation with test data and 99.58% on cross-validation with train data, respectively.

A Lightweight Multi-Mental Disorders Detection Method Using Entropy-Based Matrix from Single-Channel EEG Signals.

Background: Mental health issues are increasingly prominent worldwide, posing significant threats to patients and deeply affecting their families and social relationships. Traditional diagnostic methods are subjective and delayed, indicating the need for an objective and effective early diagnosis method. Methods: To this end, this paper proposes a lightweight detection method for multi-mental disorders with fewer data sources, aiming to improve diagnostic procedures and enable early patient detection. First, the proposed method takes Electroencephalography (EEG) signals as sources, acquires brain rhythms through Discrete Wavelet Decomposition (DWT), and extracts their approximate entropy, fuzzy entropy, permutation entropy, and sample entropy to establish the entropy-based matrix. Then, six kinds of conventional machine learning classifiers, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Naive Bayes (NB), Generalized Additive Model (GAM), Linear Discriminant Analysis (LDA), and Decision Tree (DT), are adopted for the entropy-based matrix to achieve the detection task. Their performances are assessed by accuracy, sensitivity, specificity, and F1-score. Concerning these experiments, three public datasets of schizophrenia, epilepsy, and depression are utilized for method validation. Results: The analysis of the results from these datasets identifies the representative single-channel signals (schizophrenia: O1, epilepsy: F3, depression: O2), satisfying classification accuracies (88.10%, 75.47%, and 89.92%, respectively) with minimal input. Conclusions: Such performances are impressive when considering fewer data sources as a concern, which also improves the interpretability of the entropy features in EEG, providing a reliable detection approach for multi-mental disorders and advancing insights into their underlying mechanisms and pathological states.

Firing rate model for brain rhythms controlled by astrocytes

Firing rate model for brain rhythms controlled by astrocytes

Electroencephalography Emotion Recognition Based on Rhythm Information Entropy Extraction

Electroencephalography (EEG) is a physiological signal directly generated by the central nervous system. Brain rhythm is closely related to a person’s emotional state and is widely used for EEG emotion recognition. In previous studies, the rhythm specificity between different brain channels was seldom explored. In this paper, the rhythm specificity of brain channels is studied to improve the accuracy of EEG emotion recognition. Variational mode decomposition is used to decompose rhythm signals and enhance features, and two kinds of information entropy, i.e., differential entropy (DE) and dispersion entropy (DispEn) are extracted. The rhythm being used to get the best result of single channel emotion recognition is selected as the representative rhythm, and the remove one method is employed to obtain rhythm information entropy feature. In the experiment, the DEAP database was used for EEG emotion recognition in valence-arousal space. The results showed that the best result of rhythm DE feature classification in the valence dimension is 77.04%, and the best result of rhythm DispEn feature classification in the arousal dimension is 79.25%.

The role of impulsivity and binge eating in outpatients with overweight or obesity: an EEG temporal discounting study

BackgroundBinge eating (BE) is associated with a range of cognitive control deficits related to impulsivity, including lower response inhibition, preference for immediate gratification, and maladaptive decision-making. The aim was to investigate whether impulsivity and BE may interact with the decision process and underlying brain activity in outpatients with overweight or obesity who are starting a treatment to achieve weight loss.MethodsA sample of 26 treatment-seeking outpatients with overweight or obesity was evaluated for impulsivity, BE, and temporal discounting rates. Impulsivity was measured with the Barratt Impulsiveness Scale (BIS-11), according to which two groups were composed: high BIS and low BIS; BE was assessed with the eating disorders module of the Structured Clinical Interview for DSM5—Research Version, according to which two groups were composed: with (BE group) or without BE (NBE group). Changes in subjective value of rewards were measured with the Temporal Discounting Task (TDt) where participants had to choice between sooner but smaller vs. later but larger monetary rewards. These choices were made in two differently delayed conditions (“Now” and “Not-now”). Brain rhythms were recorded through high-density electroencephalogram (hd-EEG) during the TDt.ResultsPatients with BE reported more impulsive tendencies and perceived sooner rewards as more gratifying when both options were delayed (Not-now condition, p = 0.02). The reward choice in the TDt was accompanied by a general EEG alpha band desynchronization in parietal areas observed without differences between experimental conditions and patients groups. No effects were observed within the Now condition or in the other EEG bands.ConclusionsThe tendency to favor immediate rewards may constitute an obstacle to adhering to treatment plans and achieving weight loss goals for outpatients with overweight or obesity. Clinicians are therefore encouraged to include psychological factors, such as impulsivity and dysfunctional eating behaviors, when designing weight loss programs. By addressing these psychological aspects, clinicians can better support patients in overcoming barriers to adherence and achieving sustainable weight loss.Trial registrationThis study was approved by the Ethics Committee of the Department of Psychological, Health, and Territorial Sciences of the University G. d’Annunzio of Chieti-Pescara (Prot. n. 254 of 03/14/2017).

QMWT: Design of an Improved EEG Classification Model via Q-Learning Based Processing of Multispectral Wave Traits

Electroencephalogram (EEG) signals represent various wave patterns that assist in identification of normal & abnormal brain activities. These wave patterns consist of alpha waves, which indicate relaxation phases, beta waves, that indicate normal brain rhythms & can be disturbed due to cortical & other damages, delta waves, which are dominant in infants during sleep, & theta waves, which represent irregular metabolic & hydrocephalus activities in adults. A combination of these waves is capable of representing multiple brain conditions for both adolescents & adults. Various models have been proposed by researchers to analyze these signals, and most of them work on single or bi-domain features, which limits their classification performance. Most of these models are also static, and do not incorporate continuous feedback & incremental processes. Due to which their precision & recall performance is either constant or reduces w.r.t. newer evaluations. To overcome these limitations, this text proposes design of a novel EEG classification model that uses Q-Learning for classification of multispectral feature sets. The model extracts Mel Frequency Cepstral Coefficients (MFCC), and iVector features from raw EEG data, which assists in multispectral representation of these signals. The extracted features are classified via Q-Learning based Recurrent Neural Network (RNN) classifier, that combines Gated Recurrent Unit (GRU), and Long-Short-Term Memory (LSTM) based feature sets. Due to extraction of MFCC, the GRU & LSTM Models are able to identify power spectral variation, cepstral variations, spectrogram patterns, DCT (Discrete Cosine Transform) variations, etc. While, due to iVector, entropy variations are recognized and processed for better accuracy levels. Thus, both LSTM & GRU Models assist in augmenting extracted features, which improves feature variance for better classification performance. Results of these classifications are feedback into the training set via a correlation-based analysis layer, that assists in continuously improving precision & recall performance for different evaluations. Due to incorporation of this layer, the model is capable of improving precision by 8.5%, recall by 8.3%, and accuracy by 4.9% when compared with various state-of-the-art models. It was also observed that this performance incrementally improves w.r.t. number of evaluations, which assists in deploying the model for real-time applications.

Weak signal detection capacity of type-II Morris–Lecar neuron system under presynaptic bombardments

The information encoding mechanism of the neuronal system is quite complex and is sensitive to environmental conditions. Neuronal noise, especially synaptic random inputs, is a natural part of the brain. Theoretical and experimental studies have revealed that noise plays a vital role in signal frequency selectivity and weak sensory input perception of the neuronal system. There is a consensus that sufficiently intense noise can trigger rhythmic brain activity, and these noise-induced oscillations could improve signal processing of the neuronal system, particularly when the external signal frequency aligns with the frequency of the noise-induced rhythm. Such behavior in response to noise in the signal-processing mechanism of biological systems is elucidated by stochastic resonance. In this regard, this paper systematically analyzes the stochastic resonance in a Type-II Morris–Lecar neuron under excitatory and inhibitory background spike bombardments. Our results indicate that Morris–Lecar Type II neurons enhance their signal coding capacity by exploiting background synaptic activity. In addition, it has been revealed that the biological properties of the background synaptic connections of neurons that oscillate at different frequencies, such as delta, theta, alpha, beta, and gamma bands, are different. On the other hand, it has been found that neurons adapt to the properties of pre-synaptic neurons to encode weak signals. This study ensures novel insights into the functional role of background synaptic input in neuronal coding mechanisms by demonstrating a biophysical factor of stochastic resonance via numerical analyses. Furthermore, our results show that the properties of the background synaptic inputs significantly determine what kind of signal to encode or not. These results are an important indicator of the signal frequency selectivity of neurons, such as auditory cells.

Multi-Frequency Entropy for Quantifying Complex Dynamics and Its Application on EEG Data.

Multivariate entropy algorithms have proven effective in the complexity dynamic analysis of electroencephalography (EEG) signals, with researchers commonly configuring the variables as multi-channel time series. However, the complex quantification of brain dynamics from a multi-frequency perspective has not been extensively explored, despite existing evidence suggesting interactions among brain rhythms at different frequencies. In this study, we proposed a novel algorithm, termed multi-frequency entropy (mFreEn), enhancing the capabilities of existing multivariate entropy algorithms and facilitating the complexity study of interactions among brain rhythms of different frequency bands. Firstly, utilizing simulated data, we evaluated the mFreEn's sensitivity to various noise signals, frequencies, and amplitudes, investigated the effects of parameters such as the embedding dimension and data length, and analyzed its anti-noise performance. The results indicated that mFreEn demonstrated enhanced sensitivity and reduced parameter dependence compared to traditional multivariate entropy algorithms. Subsequently, the mFreEn algorithm was applied to the analysis of real EEG data. We found that mFreEn exhibited a good diagnostic performance in analyzing resting-state EEG data from various brain disorders. Furthermore, mFreEn showed a good classification performance for EEG activity induced by diverse task stimuli. Consequently, mFreEn provides another important perspective to quantify complex dynamics.

Improved motor imagery training for subject's self-modulation in EEG-based brain-computer interface.

For the electroencephalogram- (EEG-) based motor imagery (MI) brain-computer interface (BCI) system, more attention has been paid to the advanced machine learning algorithms rather than the effective MI training protocols over past two decades. However, it is crucial to assist the subjects in modulating their active brains to fulfill the endogenous MI tasks during the calibration process, which will facilitate signal processing using various machine learning algorithms. Therefore, we propose a trial-feedback paradigm to improve MI training and introduce a non-feedback paradigm for comparison. Each paradigm corresponds to one session. Two paradigms are applied to the calibration runs of corresponding sessions. And their effectiveness is verified in the subsequent testing runs of respective sessions. Different from the non-feedback paradigm, the trial-feedback paradigm presents a topographic map and its qualitative evaluation in real time after each MI training trial, so the subjects can timely realize whether the current trial successfully induces the event-related desynchronization/event-related synchronization (ERD/ERS) phenomenon, and then they can adjust their brain rhythm in the next MI trial. Moreover, after each calibration run of the trial-feedback session, a feature distribution is visualized and quantified to show the subjects' abilities to distinguish different MI tasks and promote their self-modulation in the next calibration run. Additionally, if the subjects feel distracted during the training processes of the non-feedback and trial-feedback sessions, they can execute the blinking movement which will be captured by the electrooculogram (EOG) signals, and the corresponding MI training trial will be abandoned. Ten healthy participants sequentially performed the non-feedback and trial-feedback sessions on the different days. The experiment results showed that the trial-feedback session had better spatial filter visualization, more beneficiaries, higher average off-line and on-line classification accuracies than the non-feedback session, suggesting the trial-feedback paradigm's usefulness in subject's self-modulation and good ability to perform MI tasks.