Modulation of attention in healthy children using a course of EEG-feedback sessions

We studied changes in the power spectra of EEG in the course of sessions of feedback by EEG characteristics (neurofeedback sessions) and estimated the effects of neurofeedback on psychological and EEG correlates of voluntary attention. Indices of the latter were estimated using Bourdon’s test (a correcture test) and Schulte’s tables. Twenty-nine reasonably healthy 10-to 13-year-old children took part in the study; they were divided into two groups, an experimental group (n = 12) and a control group (n = 15). The results obtained support the statement on noticeable changes in the functional state of the brain both immediately in the course of a neurofeedback session and after a course of such trainings. Changes in the ratios of the spectral powers of the beta₁ vs theta rhythms and the low-frequency beta vs theta rhythms were found in EEG recorded from the sensorimotor zone of the right hemisphere (C4). The observed changes in the spectral characteristics of EEG induced by the course of neurofeedback sessions were accompanied by the improvement of a few indices of voluntary attention. Neirofiziologiya/Neurophysiology, Vol. 38, Nos. 5/6, pp. 458–465, September–December, 2006.     

Adaptive method for EEG spectrum component decomposition

A new computerized method for EEG rhythms extraction is proposed as a development of the idea of adjustable boundaries of frequency components that was put forward in previous investigations. Principle component analysis of the correlation matrix of EEG spectra with subsequent rotation of factor solutions was used for decomposition of a spectrum into physically meaningful spectral components. The method was tested on EEG of 14 healthy subjects recorded in 17 functional waking states. Fourteen independent spectral components in the spectral range from 0 to 100 Hz were extracted and their frequency boundaries were consistent with the current knowledge on frequency components of EEG oscillations. Main advantage of the described method is the adjustable estimation of EEG frequency oscillators taking into account characteristic properties of individual EEGs. Possible area of application might be the correct evaluation of spectral power of the EEG rhythms, EEG coherence and other spectral characteristics in clinical and experimental research, studies of the frequency characteristics of the EEG rhythms in different human functional states, changes in frequency characteristics of the EEG rhythms during maturation and in mental pathology.     

Background activity of the brain in healthy mental aging

The EEG mapping study tested age-related changes in power of EEG rhythms from delta to gamma ranges under healthy cognitive aging associated with preserved cognitive abilities and involvement in complex professional activity. 32 subjects of higher age group (HAG, mean age 65.1 +/- 1.18, 14 men and 18 women) and 33 subjects of lower age group (LAG mean age 22.1 +/- 0.38, 18 men and 15 women) participated in the study. Mean power of slow (delta, theta and alpha2) activity decreased and of fast activity (beta, gamma) increased as subject age increased. Compared to subjects of LAG subjects of HAG displayed a reduction in heterogeneity of EEG activity across recording sites. Centro-temporal gradients of power for frequency ranges from delta to beta2 and frontoparietal gradients and hemispheric asymmetry for alpha and beta1 rhythms were smoothed in subjects of HAG. These results suggest that observed age-related changes in baseline EEG may be the prerequisite for compensatory neural recruitment that may be associated as with allocation of more resources in cognitive processes so with reorganization of cortical networks including areas susceptible to physiological changes with aging.     

Selection of relevant features for EEG signal classification of schizophrenic patients

In this paper, EEG signals of 20 schizophrenic patients and 20 age-matched control participants are analyzed with the objective of determining the more informative channels and finally distinguishing the two groups. For each case, 22 channels of EEG were recorded. A two-stage feature selection algorithm is designed, such that, the more informative channels are first selected to enhance the discriminative information. Two methods, bidirectional search and plus-L minus-R (LRS) techniques are employed to select these informative channels. The interesting point is that most of selected channels are located in the temporal lobes (containing the limbic system) that confirm the neuro-phychological differences in these areas between the schizophrenic and normal participants. After channel selection, genetic algorithm (GA) is employed to select the best features from the selected channels. In this case, in addition to elimination of the less informative channels, the redundant and less discriminant features are also eliminated. A computationally fast algorithm with excellent classification results is obtained. Implementation of this efficient approach involves several features including autoregressive (AR) model parameters, band power, fractal dimension and wavelet energy. To test the performance of the final subset of features, classifiers including linear discriminant analysis (LDA) and support vector machine (SVM) are employed to classify the reduced feature set of the two groups. Using the bidirectional search for channel selection, a classification accuracy of 84.62% and 99.38% is obtained for LDA and SVM, respectively. Using the LRS technique for channel selection, a classification accuracy of 88.23% and 99.54% is also obtained for LDA and SVM, respectively. Finally, the results are compared and contrasted with two well-known methods namely, the single-stage feature selection (evolutionary feature selection) and principal component analysis (PCA)-based feature selection. The results show improved accuracy of classification in relatively low computational time with the two-stage feature selection.     

Individual typological features in the EEG of athletes after acute hypoxic treatment

In athletes with different types of physical training and various temperaments, variability of the main rhythms of the EEG (??, ??, and ??) and the parameters of the desynchronization response of the ??-rhythm, the individual extent of the decrease in the power of the ??-rhythm during eye opening under the conditions of hypoxia growing from 20.9 to 10% of O₂ were studied. Twenty-four 18- to 26-year-old athletes, including 11 swimmers and 13 skiers, were involved in the study. We found that, in contrast to normoxia conditions, hypoxia was associated with the instability of the spectrum of the EEG rhythms and phasic changes during a 25-min hypoxia. The EEG response during hypoxia depended on individual typological features estimated using Strelau??s temperament questionnaire. Negative correlation between the psychological construct ??endurance?? measured using the FCB-TI questionnaire (Formal Characteristics of Behavior-Temperament Inventory) and the power of the EEG ??-rhythm was found. The parameters of training and the features of the respiration pattern that appeared as a consequence of training modulated the sensitivity of brain structures to hypoxia, which was reflected in the changes in the EEG ??-rhythm under hypoxia conditions.     

Assessing a learning process with functional ANOVA estimators of EEG power spectral densities

We propose to assess the process of learning a task using electroencephalographic (EEG) measurements. In particular, we quantify changes in brain activity associated to the progression of the learning experience through the functional analysis-of-variances (FANOVA) estimators of the EEG power spectral density (PSD). Such functional estimators provide a sense of the effect of training in the EEG dynamics. For that purpose, we implemented an experiment to monitor the process of learning to type using the Colemak keyboard layout during a twelve-lessons training. Hence, our aim is to identify statistically significant changes in PSD of various EEG rhythms at different stages and difficulty levels of the learning process. Those changes are taken into account only when a probabilistic measure of the cognitive state ensures the high engagement of the volunteer to the training. Based on this, a series of statistical tests are performed in order to determine the personalized frequencies and sensors at which changes in PSD occur, then the FANOVA estimates are computed and analyzed. Our experimental results showed a significant decrease in the power of \(\beta\) and \(\gamma\) rhythms for ten volunteers during the learning process, and such decrease happens regardless of the difficulty of the lesson. These results are in agreement with previous reports of changes in PSD being associated to feature binding and memory encoding.     

Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

To investigate the abnormal brain activities in the early stage of Parkinson’s disease (PD), the electroencephalogram (EEG) signals were recorded with 20 channels from non-dementia PD patients (18 patients, 8 females) and age matched healthy controls (18 subjects, 8 females) during the resting state. Two methods based on the ordinal patterns of the recorded series, i.e., permutation entropy (PE) and order index (OI), were introduced to characterize the complexity of the cortical activities for two groups. It was observed that the resting-state EEG of PD patients showed lower PE and higher OI than healthy controls, which indicated that the early-stage PD caused the reduced complexity of EEG. We further applied two methods to determine the complexity of EEG rhythms in five sub-bands. The results showed that the gamma, beta and alpha rhythms of PD patients were characterized by lower PE and higher OI, i.e., reduced complexity, than healthy subjects. No significant differences were observed in theta or delta rhythms between two groups. The findings suggested that PE and OI were promising methods to detect the abnormal changes in the dynamics of EEG signals associated with early-stage PD. Further, such changes in EEG complexity may be the early markers of the cortical or subcortical dysfunction caused by PD.     

Noisy Galvanic Vestibular Stimulation Modulates the Amplitude of EEG Synchrony Patterns

Noisy galvanic vestibular stimulation has been associated with numerous cognitive and behavioural effects, such as enhancement of visual memory in healthy individuals, improvement of visual deficits in stroke patients, as well as possibly improvement of motor function in Parkinson’s disease; yet, the mechanism of action is unclear. Since Parkinson’s and other neuropsychiatric diseases are characterized by maladaptive dynamics of brain rhythms, we investigated whether noisy galvanic vestibular stimulation was associated with measurable changes in EEG oscillatory rhythms within theta (4–7.5 Hz), low alpha (8–10 Hz), high alpha (10.5–12 Hz), beta (13–30 Hz) and gamma (31–50 Hz) bands. We recorded the EEG while simultaneously delivering noisy bilateral, bipolar stimulation at varying intensities of imperceptible currents – at 10, 26, 42, 58, 74 and 90% of sensory threshold – to ten neurologically healthy subjects. Using standard spectral analysis, we investigated the transient aftereffects of noisy stimulation on rhythms. Subsequently, using robust artifact rejection techniques and the Least Absolute Shrinkage Selection Operator regression and cross-validation, we assessed the combinations of channels and power spectral features within each EEG frequency band that were linearly related with stimulus intensity. We show that noisy galvanic vestibular stimulation predominantly leads to a mild suppression of gamma power in lateral regions immediately after stimulation, followed by delayed increase in beta and gamma power in frontal regions approximately 20–25 s after stimulation ceased. Ongoing changes in the power of each oscillatory band throughout frontal, central/parietal, occipital and bilateral electrodes predicted the intensity of galvanic vestibular stimulation in a stimulus-dependent manner, demonstrating linear effects of stimulation on brain rhythms. We propose that modulation of neural oscillations is a potential mechanism for the previously-described cognitive and motor effects of vestibular stimulation, and noisy galvanic vestibular stimulation may provide an additional non-invasive means for neuromodulation of functional brain networks.     

Specific features of the oscillatory brain activity during the final stage of creative problem solving in young and elderly subjects

The neurophysiological mechanisms underlying retention of creative potential during aging are still poorly studied. Previously, we have identified age-related changes in the temporal dynamics of brain activity and the speed of creative problem solving at its initial stage, suggesting that younger and older subjects used different strategies. These differences in strategies may also be observed at the final stage of problem solving. Therefore, we have studied the pattern of temporal changes in the EEG spectral characteristics (event-related spectral perturbation, ERSP) in younger (N = 89, 22.1 ± 3.2 years) and older (N = 90, 64.9 ± 6.7 years) age cohorts during 600 ms before the preparation to motor response, which indicates that solution is found. The general and ageand sex-related features of the oscillatory brain activity at the final stage of problem solving were revealed. All subjects displayed statistically significant EEG temporal dynamics associated with a reduction of power reactivity of rhythms prior to the response. The age-related differences included more pronounced ERSP frontal–parietal gradient in the θ frequency range and lower ERSP values in the β frequency range in elderly subjects as compared with the younger individuals. The most pronounced age-related differences in the β₁ rhythm were observed in the posterior cortex. The age-related differences in the α₃ frequency range were mediated by the sex factor: lateral differences were pronounced only in young men, and the coefficient of hemispheric asymmetry in this group differed significantly from that in older men and younger women. These data reflect the changes in EEG that were associated with the evaluation of creative idea, making a decision about completion of the search, and intention to make a motor response that indicates that solution is found.     

Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing

Brain computer interfaces (BCI) provide a new approach to human computer communication, where the control is realised via performing mental tasks such as motor imagery (MI). In this study, we investigate a novel method to automatically segment electroencephalographic (EEG) data within a trial and extract features accordingly in order to improve the performance of MI data classification techniques. A new local discriminant bases (LDB) algorithm using common spatial patterns (CSP) projection as transform function is proposed for automatic trial segmentation. CSP is also used for feature extraction following trial segmentation. This new technique also allows to obtain a more accurate picture of the most relevant temporal–spatial points in the EEG during the MI. The results are compared with other standard temporal segmentation techniques such as sliding window and LDB based on the local cosine transform (LCT).     

EEGgui: a program used to detect electroencephalogram anomalies after traumatic brain injury

Background

Identifying and quantifying pathological changes in brain electrical activity is important for investigations of brain injury and neurological disease. An example is the development of epilepsy, a secondary consequence of traumatic brain injury. While certain epileptiform events can be identified visually from electroencephalographic (EEG) or electrocorticographic (ECoG) records, quantification of these pathological events has proved to be more difficult. In this study we developed MATLAB-based software that would assist detection of pathological brain electrical activity following traumatic brain injury (TBI) and present our MATLAB code used for the analysis of the ECoG.

Methods

Software was developed using MATLAB(?) and features of the open access EEGLAB. EEGgui is a graphical user interface in the MATLAB programming platform that allows scientists who are not proficient in computer programming to perform a number of elaborate analyses on ECoG signals. The different analyses include Power Spectral Density (PSD), Short Time Fourier analysis and Spectral Entropy (SE). ECoG records used for demonstration of this software were derived from rats that had undergone traumatic brain injury one year earlier.

Results

The software provided in this report provides a graphical user interface for displaying ECoG activity and calculating normalized power density using fast fourier transform of the major brain wave frequencies (Delta, Theta, Alpha, Beta1, Beta2 and Gamma). The software further detects events in which power density for these frequency bands exceeds normal ECoG by more than 4 standard deviations. We found that epileptic events could be identified and distinguished from a variety of ECoG phenomena associated with normal changes in behavior. We further found that analysis of spectral entropy was less effective in distinguishing epileptic from normal changes in ECoG activity.

Conclusion

The software presented here was a successful modification of EEGLAB in the Matlab environment that allows detection of epileptiform ECoG signals in animals after TBI. The code allows import of large EEG or ECoG data records as standard text files and uses fast fourier transform as a basis for detection of abnormal events. The software can also be used to monitor injury-induced changes in spectral entropy if required. We hope that the software will be useful for other investigators in the field of traumatic brain injury and will stimulate future advances of quantitative analysis of brain electrical activity after neurological injury or disease.

     

The Genesis of the EEG and its Relation to Electromagnetic Radiation

The dominant frequencies in the human electroencephlogram (EEG) are 8–13 Hz (Alpha), 4–7 Hz (Theta), less than 4 Hz (Delta), and greater than 13 Hz (Beta). The conventional explanation of the mechanism for these dominant rhythms involves the effect of electrical activity i n the thalamus on the cortical synaptic potentials that are recorded in an EEG (1,2). Although electrical activity in the thalamus is of prime importance in determining what is recorded Ly the EEG, it is not known why the dominant rhythms recorded are of those specific frequencies. These dominant frequencies may be related through evolution to some aspect of the environment. This paper is devoted to a consideration of the possible relation between the brain's electrical activity and external electromagnetic fields.     

Feature selection for sleep/wake stages classification using data driven methods

This paper focuses on the problem of selecting relevant features extracted from human polysomnographic (PSG) signals to perform accurate sleep/wake stages classification. Extraction of various features from the electroencephalogram (EEG), the electro-oculogram (EOG) and the electromyogram (EMG) processed in the frequency and time domains was achieved using a database of 47 night sleep recordings obtained from healthy adults in laboratory settings. Multiple iterative feature selection and supervised classification methods were applied together with a systematic statistical assessment of the classification performances. Our results show that using a simple set of features such as relative EEG powers in five frequency bands yields an agreement of 71% with the whole database classification of two human experts. These performances are within the range of existing classification systems. The addition of features extracted from the EOG and EMG signals makes it possible to reach about 80% of agreement with the expert classification. The most significant improvement on classification accuracy is obtained on NREM sleep stage I, a stage of transition between sleep and wakefulness.     

Neurofeedback training produces normalization in behavioural and electrophysiological measures of high-functioning autism

Autism spectrum disorder (ASD) is a neurodevelopmental condition exhibiting impairments in behaviour, social and communication skills. These deficits may arise from aberrant functional connections that impact synchronization and effective neural communication. Neurofeedback training (NFT), based on operant conditioning of the electroencephalogram (EEG), has shown promise in addressing abnormalities in functional and structural connectivity. We tested the efficacy of NFT in reducing symptoms in children with ASD by targeting training to the mirror neuron system (MNS) via modulation of EEG mu rhythms. The human MNS has provided a neurobiological substrate for understanding concepts in social cognition relevant to behavioural and cognitive deficits observed in ASD. Furthermore, mu rhythms resemble MNS phenomenology supporting the argument that they are linked to perception and action. Thirty hours of NFT on ASD and typically developing (TD) children were assessed. Both groups completed an eyes-open/-closed EEG session as well as a mu suppression index assessment before and after training. Parents filled out pre- and post-behavioural questionnaires. The results showed improvements in ASD subjects but not in TDs. This suggests that induction of neuroplastic changes via NFT can normalize dysfunctional mirroring networks in children with autism, but the benefits are different for TD brains.     

Spectral turbulence measuring as feature extraction method from EEG on affective computing

In biomedical and psychological applications dealing with EEG, a suitable selection of the most relevant electrodes is useful for lightening the data acquisition and facilitating the signal processing. Therefore, an efficient method for extracting and selecting features from EEG channels is desirable. Classification methods are more and more applied for obtaining important conclusions from diverse psychological processes, and specifically for emotional processing. In this work, an original and straightforward method, inspired by the spectral turbulence (ST) measure from electrocardiogram and the support vector machine-recursive feature elimination (SVM-RFE) algorithm, is proposed for classifying EEG signals. The goal of this study is to introduce the ST concept in applications of artificial intelligence related to cognitive processes and to determine the best EEG channels for distinguishing between two different experimental conditions. By means of this method, the left temporal region of the brain has revealed to be greatly involved in the affective valence processing elicited by visual stimuli.     

Changes in human EEG caused by low level modulated microwave stimulation

This study focuses on the effect of low level microwave radiation on human EEG alpha and theta rhythms. During the experiment, 20 healthy volunteers were exposed to a 450 MHz microwaves with 7 Hz on-off modulation. The field power density at the scalp was 0.16 mW/cm2. Signals from the following EEG channels were used: FP1, FP2, P3, P4, T3, T4, O1, and O2. The experimental protocol consisted of one cycle of short term photic and ten cycles of the repetitive microwave stimulation. The changes caused by photic as well as microwave stimulation were more regular on the alpha rhythm. In the majority of cases, photic stimulation caused changes in the EEG energy level in the occipital and microwave stimulation in the frontal region. Our experimental results demonstrated that microwave stimulation effects became apparent, starting from the third stimulation cycle. Changes varied strongly from subject to subject. Therefore, photic and microwave exposure did not cause statistically significant changes in the EEG activity level for the whole group. For some subjects, clear tendencies of changes in microwave on-off cycles were noticeable.     

A history of recent advancements on <Emphasis Type="Italic">Nephrops norvegicus</Emphasis> behavioral and physiological rhythms

Biological rhythms are a widespread feature of living organisms, being expressed at any level of their organization. Behavioral and physiological rhythms can affect the results on species stock assessment when the timing of sampling is not taken into account. That timing is of importance since animals may be present or not in a certain area of sampling depending on their activity cycle. As an example of this, the rhythmic behavior and physiology of one of the most commercially important European decapods, the Norway lobster (Nephrops norvegicus) was studied. These rhythms affect its commercial catchability at a diel and at a seasonal scale. Nephrops inhabits muddy bottoms where animals dig burrows that save them from the trawl tow capture when occupied. Catch patterns have been widely used as proxy of activity rhythms of populations of different depths. Catches show a modulation upon the day-night cycle since animals emerge under optimum environmental illumination in order to feed. Emergence is also affected in a not fully clarified manner by other variables of environmental and demographic nature (e.g. food presence, hunger state, sex, size, reproductive stage, territorialism and mating). All these features make Nephrops a good model of reference for studies on biological rhythms of other commercially important deep water decapods in relation to their ecological context. In this review, we summarize the actual knowledge on Nephrops behavioral and physiological rhythms. We will compare data obtained from laboratory tests on single individuals with data obtained from trawling of populations of different depths. We will also describe some new hypotheses on the rhythmic regulation of the species behaviour, as well as potential scenarios for future research.     

Spatial synchronization of the segmental EEG in humans

Topographic features of spatial synchronization of sharp changes, or rapid transition processes (RTP), were studied in human EEG recorded from longitudinal and transversal electrode arrays. A new algorithm, the EEG Threshold Scanning, was proposed for the detection of the RTP. Synchronization of the RTP was estimated by Operational Synchrony Index (OSI) based on the difference between the actual and stochastic frequency of RTP coincidence in a pair of EEG channels. The relationship between the OSI and interelectrode distance was not monotonous. The OSI depended also on the extent of morpho-functional similarity between two cortical areas. Similar results were obtained for crosscorrelation calculated for the same pairs of the EEG derivations. The existence of dynamic spatial modules which incorporate different brain areas by complementary stabilization of their functional states is discussed.     

Detection of nonlinear interactions of EEG alpha waves in the brain by a new coherence measure and its application to epilepsy and anti-epileptic drug therapy

EEG and field potential rhythms established in the cortex and thalamus may accommodate the propagation of seizures. This article describes the interaction between thalamus and cortex during pentylenetetrazol (PTZ) seizures in rats with and without prior treatment with ethosuximide (ESM), a well-known antiepileptic drug (AED) that raises the threshold for seizures, was given before PTZ. The AED was given before PTZ convulsant administration. We track this thalamo-cortical association with a novel measure we have called the cross-bicoherence gain, or BISCOH. This quantity allows us to measure the spectral coherence in a purely higher order spectralmethodology. BISCOH is able to track the formation of nonlinearities at specific frequencies in the recorded EEG. BISCOH showed a strong increase in low alpha wave harmonic generationat 10 and 12.5 Hz after ESM treatment (p < 0.02 and p < 0.007, respectively). Conventional coherence failed to show distinctive and significant changes in thalamo-cortical coupling after ESM treatment at those frequencies and instead showed changes at 5 Hz. This rise in cortical rhythms is evidence of harmonic generation or new frequency formation in the thalamo-cortical system withAED therapy. BISCOH could become a powerful tool in unraveling changes in coherence due to neuroelectric modulation resulting from drug treatment or electrical stimulation.     

A method for the calculation of induced band power: implications for the significance of brain oscillations

A method for the calculation of significant changes in induced band power (IBP) is presented. In contrast to traditional measures of event-related band power (ERBP) which are composed of evoked and not evoked EEG components, the proposed measure for IBP is deprived from phase locked (or evoked) EEG activity. It is assumed that changes in IBP reflect the modulation of brain oscillations that are largely independent from ERPs. The results of a visual oddball task show that significant changes in IBP can be observed in response to the presentation of a warning signal (preceding a target or nontarget) and the imperative stimulus (i.e. a target or nontarget) in the α, θ and δ band. Only a few significant changes in IBP were obtained for the warning signal in the θ band although highly significant changes in ERBP were found. Our findings document that changes in IBP may be considered a phenomenon that is largely independent from the occurrence of ERPs. They underline the significance of oscillatory processes and suggest that induced rhythms are modulated by stimuli and/or events in a not phase locked way.