EEG/MEG Source Localization

Brahma Kumaris Rajayoga’s open eyed ‘seed stage meditation’ was studied in 52 meditators. Meditation stages: concentration on peace, realization of being a soul and communion with the Supreme Soul. Frequency domain brain electric source localization was used on multichannel EEG recordings to establish activation differences between meditation and open eyed, task-free resting. Additional exploratory analyses probed for effects of passing time from initial rest through meditation to final rest. Meditation showed reduced activity in delta and increased activity in low alpha frequencies. Affected brain areas comprised the following networks: central executive network, mirroring network, task-positive and task-negative network. These altered activations reflect the main cognitive-affective and behavioral specifics of seed stage meditation: attention modulation, self-related processing, experiencing the soul as a point of light between the eyes, endowing the soul with the properties (peac...

This study investigates the neurophysiological implications of applied acupressure to the "Lu10" acupoint. Scalp electroencephalography (EEG) was recorded from ten participants for eight sessions over a period of one month. After eliminating the artefacts with the help of FIR bandpass filter, the band power was computed for each sub-band before and after the intervention. Significant alterations in band power were observed in alpha and beta sub-band post stimuli. Results suggest that the delta and beta band power increases by 25% ±2.8 and 36.4% ±3.0 respectively. It reflects improved relaxation and improved cognitive focus post-intervention. Also, a 20% decrease in the delta-theta ratio was observed which signifies a shift from profound relaxation to a more awake and attentive state. A decrease (9.3% ±2.8) in alpha power suggests a shift from passive relaxation to more active mental involvement. The short-term appearance of beta activities reveals increased alertness without causing significant stress. These results highlight that acupressure may be used as a non-invasive treatment to improve relaxation, cognitive function, and emotional control.

Electroencephalography (EEG) provides no direct link between electrode positions and underlying signal generators. Inferences based on spatial proximity between scalp positions and cortical structures are not reliable. More accurate source localization is obtained by solving both the forward and the inverse problem, but is technically challenging. In this paper, we provide a reference table of correspondence between EEG sensors and cortical anatomical regions based on a realistic head model. We also present a universal algorithm to compute the solution by using a forward model to determine the sensitivity for electrodes of any defined electrode positioning system and cortical anatomical parcellation.

The pursuit of a unified theory that captures the intricacies of the brain and mind continues to be a significant challenge in theoretical neuroscience. This paper presents a novel, triune framework that utilizes the concept of collective biased random walk (cBRW). Our approach strives to transcend biological specifics, offering a high-level abstraction that remains general and applicable across various neural phenomena. Despite the solid traditional foundation of computational neuroscience, the intricate delicacy of neural processes calls for a renewed probabilistic approach. We aim to utilize the intuitive nature of probability concepts —such as the probability of localization and state, and uniform probability distribution— to study the stochastic organization of electric charges and signals in the brain. This electrophysiological intricacy emerges from the seemingly paradoxical reality that tiny electric events, while random, collectively give rise to predictable, long-range oscillations. These oscillations manifest in three groups of activation states. Our framework categorizes the brain as a triune system, accommodating classical, semiclassical, and non-classical interpretations of both probabilistic phenomena and cBRW models, alongside three groups of states. We conclude that by appreciating, rather than overlooking, the tiny random walks of electric charges and signals in the brain, we can gain a triune mathematical foundation for theoretical brain science, the powerful capabilities of this organ, and the electromagnetic interfaces we can develop. Manuscript received: 30-10-2023. Revised: 19-06-2024. Accepted: 21-08-2024. RESEARCH ARTICLE. Keywords. Brain States, Brain Theory, Brain Electrophysiology, Neural Computations, Electroencephalography, Non-Classic Brains. MAIN Abbreviations: BRW: Biased Random Walks, cBRW: Collective Random Walks, P(.): Probability, Pl(x): Probability of localization at x, Ps(b): Probability of state b, RW: Random Walk. OTHER Abbreviations: 1D: one Dimension, 2D: two Dimensions, DWI: diffusion weighted imaging, E(.): Expectancy, EEG: Electroencephalography, MEG: Magnetoencephalography, MRI: Magnetic Resonance Imaging, NIRS: near-infrared spectroscopy, TES/TMS: electric/magnetic stimulation, Var(.): Variance. International Group of Neuroscience, Neuroengineering and Neurophilosophy, IGN(S,E,P). Colombia, US, India, Europe.

Η παρούσα διπλωματικη εργασία πραγματεύεται τα ευθέα προβληματα των μη επεμβατικών εγκεφαλικών απεικονιστικών τεχνικών της Hλεκτροεγκεφαλογραφίας (EEG) και της Mαγνητοεγκεφαλογραφίας (MEG), στη σφαιρικη γεωμετρία. Σκοπός της διπλωματικης εργασίας είναι η μελέτη της μαθηματικης θεωρίας των ευθέων προβλημάτων της EEG και της MEG. Γίνεται μαθηματικη ανάλυση αυτών των προβλημάτων, όπου υπολογίζεται αναλυτικά το ηλεκτρικό και το μαγνητικό πεδίο που οφείλονται σε γνωστη σημειακη τετραπολικη πηγη νευρωνικού ρεύματος. Η πηγη βρίσκεται μέσα στον εγκέφαλο, ο οποίος θεωρείται ως σφαιρικός ομογενης αγωγός. Τέλος, αναφέρονται τα συμπεράσματα της παρούσας εργασίας.
Στο πρώτο κεφάλαιο παρουσιάζονται οι εξισώσεις Maxwell και η σχεδόν στάσιμη θεωρία, με την οποία προτυποποιείται η ηλεκτρομαγνητικη δραστηριότητα του εγκεφάλου. Αναφέρεται η φυσικη λειτουργία των νευρώνων και κατόπιν γίνεται η προτυποποίηση του εγκεφάλου ως αγωγού. Προσδιορίζονται οι εξισώσεις που περιγράφουν τη λειτουργία του, σε επίπεδο νευρωνικών διεγέρσεων και διάδοσης νευρωνικών ρευμάτων και ορίζονται το ευθύ και το αντίστροφο πρόβλημα της EEG και της MEG. Δίνονται οι ολοκληρωτικές αναπαραστάσεις Geselowitz για το ηλεκτρικό δυναμικό και τη μαγνητικη επαγωγη στα πλαίσια της μαθηματικης ανάλυσης των αντίστοιχων προβλημάτων. Στη συνέχεια γίνεται η δυαδικη ενοποίηση της EEG και της MEG και τέλος παρουσιάζεται το πρότυπο των πολλαπλών αγώγιμων φλοιών που περιβάλλουν τον εγκέφαλο.
Στο δεύτερο κεφάλαιο δίνεται η μαθηματικη ανάλυση του ευθέως προβληματος της Ηλεκτροεγκεφαλογραφίας στο γεωμετρικό πρότυπο της ομογενούς σφαίρας. Γίνεται πληρης ανάλυση για το εσωτερικό και το εξωτερικό πρόβλημα του ηλεκτρικού δυναμικού όπου η νευρωνικη διέγερση οφείλεται σε τετράπολο ρεύματος στο εσωτερικό του εγκεφάλου με γνωστη ροπη.
Στο τρίτο κεφάλαιο δίνεται η μαθηματικη ανάλυση του ευθέως προβληματος της Μαγνητοεγκεφαλογραφίας, επίσης στο γεωμετρικό πρότυπο της ομογενούς σφαίρας. Υπολογίζεται το εξωτερικό μαγνητικό δυναμικό και το αντίστοιχο μαγνητικό πεδίο όπου η νευρωνικη διέγερση οφείλεται σε τετράπολο ρεύματος στο εσωτερικό του εγκεφάλου με γνωστη ροπη.
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Στο τέταρτο κεφάλαιο παρατίθενται τα συμπεράσματα και η διπλωματικη εργασία ολοκληρώνεται με το Παράρτημα Δ, όπου αναπτύσσεται μια ολοκληρωμένη μελέτη εύρεσης της μαγνητικης επαγωγης με τη χρηση πολυπολικού αναπτύγματος.

This thesis addresses the forward problems of noninvasive brain imaging techniques of Electroencephalography (EEG) and Magnetoencephalography (MEG), in spherical geometry. The purpose of this thesis is to study the mathematical theory of the forward problems of Electroencephalography and Magnetoencephalography. Mathematical analysis of these problems is done, calculating, in detail, the electric and magnetic field due to a known localized quadrupole current source. The source is located within the brain, which is considered as homogeneous spherical conductor. Finally, the conclusions of this work are refered.
In first chapter, are presented the Maxwell equations and the quasi static theory, which models the electromagnetic activity of the brain. Then reference is made to the natural functioning of neurons and the brain is modeled as a conductor. The equations that describe the neural impulses and the propagation of neural current ere given and then are defined the forward and the inverse problems of Electroengephalography and Magnetoencephalography. The integral Geselowitz formula's are given for electric potential and magnetic induction within the mathematical analysis of the corresponding problems. The dyadic integration of Electroencephalography and Magnetoencephalography is made and finally is presented the model of multiple conductive cortex surrounding the brain.
In second chapter is given the mathematical analysis of the forward problem of Electroencephalography in the geometric model of a homogeneous sphere. The internal and external problem of electrical potential are calculated, with neuronal excitation which is due to a quadrupole inside the brain with known moment.
Ιn third chapter is given the mathematical analysis of the forward problem of Magnetoencephalography, in the geometric model of a homogeneous sphere. The external magnetic potential is calculated and the corresponding magnetic field where the neuronal excitation, is due to a quadrupole inside the brain with known moment.
The fourth chapter presents the conclusions and the thesis concludes with Appendix D, in which a comprehensive study, finding the magnetic induction using multipolar expansion is developed.

BrainRap represents a significant advancement in the integration of machine learning, brain-computer interfaces (BCIs), and neuroscience to enhance artistic creativity and predict the commercial value of music. This white paper details the technical methodologies employed by BrainRap, including real-time EEG data processing, neurochemical insights, and advanced machine learning models. By leveraging these technologies, BrainRap not only supports artists in their creative endeavors but also offers a robust framework for music valuation, guiding investment decisions in the music industry. The technology builds on foundational research in semantic mapping and neurochemical modulation, providing a comprehensive tool for artists, investors, and financial institutions to enhance creative expression and predict music success accurately.

A novel evanescent-mode metamaterial resonator based oscillator is discussed for current and later generation wireless communication systems. The reported paper features tunable multi-band oscillators with significantly reduced size and power consumption compared to separate oscillators for use in multiple bands, multi-mode wireless communication systems.

Visual cues from the speaker’s face influence the perception of speech. An example of this influence is demonstrated by the McGurk-effect where illusory (cross-modal) sounds are perceived following presentation of incongruent audio–visual (AV) stimuli. Previous studies report the engagement of specific cortical modules that are spatially distributed during cross-modal perception. However, the limits of the underlying representational space and the cortical network mechanisms remain unclear. In this combined psychophysical and electroencephalography (EEG) study, the participants reported their perception while listening to a set of synchronous and asynchronous incongruent AV stimuli. We identified the neural representation of subjective cross-modal perception at different organizational levels — at specific locations in sensor space and at the level of the large-scale brain network estimated from between-sensor interactions. We identified an enhanced positivity in the event-related p...

Amnestic mild cognitive impairment (aMCI) is a clinical condition characterized by memory impairment in the absence of any other cognitive impairment and is commonly associated with high conversion to Alzheimer's disease. Recent evidence shows that executive functions and selective attention mechanisms could also be impaired in aMCI. In this study, we investigated performance differences (i.e., reaction times [RTs] and accuracy) between a group of aMCI participants and a group of age-matched healthy individuals on the attentional network task (ANT) focusing on situations with increased interference. In particular, we assessed the relationship between interference and conflict effects and grey matter volumes (GMVs) of the anterior cingulate cortex (ACC)/presupplementary motor area in the entire sample because of its crucial role in conflict monitoring. When compared with controls, aMCI participants were less accurate on the ANT, showing increased interference and conflict effects, but no differences in RTs. In addition, aMCI participants exhibited lower GMV in the ACC than controls. While better accuracy for interference and conflict effects was associated with an increase of GMV in the ACC for both groups, RTs from the interference effect were negatively correlated with GMV of the ACC only in aMCI participants. In other words, lower GMV values of the ACC were paralleled with significantly impaired performance in terms of interference resolution. In conclusion, our study suggests the presence of a selective impairment in interference and conflict monitoring in aMCI, which in turn is associated with decreased GMVs in the ACC. Amnestic mild cognitive impairment (aMCI) is characterized by memory impairment in the absence of any other cognitive impairment and is associated with normal activities of daily living (Petersen, 2004; Petersen et al., 2001). It is commonly associated with a high risk of conversion to Alzheimer's disease (AD; Petersen, 2004). As episodic memory

The wavelet Maximum Entropy on the Mean (wMEM) approach to the MEG inverse problem is revisited and extended to infer brain activity from full space-time data. The resulting dimensionality increase is tackled using a collection of techniques, that includes time and space dimension reduction (using respectively wavelet and spatial filter based reductions), Kronecker product modeling for covariance matrices, and numerical manipulation of the free energy directly in matrix form. This leads to a smooth numerical optimization problem of reasonable dimension, solved using standard approaches. The method is applied to the MEG inverse problem. Results of a simulation study in the context of slow wave localization from sleep MEG data are presented and discussed.

Attention deficit hyperactivity disorder is a complex brain disorder which is usually difficult to diagnose. As a result many literature reports about the increasing rate of misdiagnosis of ADHD disorder with other types of brain disorder. There is also a risk of normal children to be associated with ADHD if practical diagnostic criteria are not supported. To this end we propose a decision support system in diagnosing of ADHD disorder through brain electroencephalographic signals. Subjects of 10 children participated in this study, 7 of them were diagnosed with ADHD disorder and remaining 3 children are normal group. Our main goal of this sthudy is to present a supporting diagnostic tool that uses signal processing for feature selection and machine learning algorithms for diagnosis.Particularly, for a feature selection we propose information theoretic which is based on entropy and mutual information measure. We propose a maximal discrepancy criterion for selecting distinct (most distinguishing) features of two groups as well as a semi-supervised formulation for efficiently updating the training set. Further, support vector machine classifier trained and tested for identification of robust marker of EEG patterns for accurate diagnosis of ADHD group. We demonstrate that the applicability of the proposed approach pro

Channel state information (CSI) collected during WiFi packet transmissions can be used for localization of commodity WiFi devices in indoor environments with multipath propagation. To this end, the angle of arrival (AoA) and time of flight (ToF) for all dominant multipath components need to be estimated. A two-dimensional (2D) version of the multiple signal classification (MUSIC) algorithm has been shown to solve this problem using 2D grid search, which is computationally expensive and is therefore not suited for real-time localisation. In this paper, we propose using a modified matrix pencil (MMP) algorithm instead. Specifically, we show that the AoA and ToF estimates can be found independently of each other using the one-dimensional (1D) MMP algorithm and the results can be accurately paired to obtain the AoA-ToF pairs for all multipath components. Thus, the 2D estimation problem reduces to running 1D estimation multiple times, substantially reducing the computational complexity. We identify and resolve the problem of degenerate performance when two or more multipath components have the same AoA. In addition, we propose a packet aggregation model that uses the CSI data from multiple packets to improve the performance under noisy conditions. Simulation results show that our algorithm achieves two orders of magnitude reduction in the computational time over the 2D MUSIC algorithm while achieving similar accuracy. High accuracy and low computation complexity of our approach make it suitable for applications that require location estimation to run on resource-constrained embedded devices in real time.

Recently we have developed a method for electroencephalogram (EEG) dipole source localization based on particle filtering (PF). In this study the method is combined with beamforming to eliminate the noise which is spatially uncorrelated with the desired signal and accordingly to improve its performance. The proposed beamforming is an optimum, linear and data independent filter which can be applied to stationary as well as non-stationary data. Simulation and real data results have been provided to show its better performance over PF and beamforming approaches for dipole source localization.

Several studies have shown physical and mental health benefits associated with different schools of meditation. However, reviews have recommended interpretation of study outcomes in the context of school specific traditional practices. In the literature, practice proficiency is often conflated with hours or years of meditation practice and it is difficult to attribute wellbeing gains directly to the proficiency of practice. We carried out two studies to examine wellbeing, practice proficiency, and duration of practice amongst Brahma Kumaris Rajayoga (BKRY) practitioners from India. In Study 1, we surveyed participants (n = 1009) to record the socio-demographics, spiritual lifestyle components, and wellbeing enhancement attributed to their spiritual practice. We developed new measures for evaluating proficiency of BKRY practice and for assessment of progress in wellbeing attributed to BKRY practice. Both measures had high internal consistency scores (alphas 0.95, 0.93). Block-wise hierarchical linear regression analysis showed that after accounting for demographic variables, BKRY practice proficiency strongly predicted progress in wellbeing, explaining over 51.6% of the total variance. In Study 2, we took three well-matched groups (n = 86) that were distinct on the basis of duration of practice (long term, short term and zero term). We examined wellbeing differences in these three groups and the extent of correlation of wellbeing with practice proficiency, progress in wellbeing and duration of practice. We conclude that meditation practice does improve wellbeing and that proficiency of practice is better correlated with most aspects of wellbeing than duration of practice but there are a few aspects of wellbeing that increase with sustained practice over a long time. We recommend that studies involving other schools of spiritual/religious practice incorporate tradition-specific proficiency assessment (instead of using years of practice as a proxy) and also use the Progress in Wellbeing scale for assessing practiceaided improvements in wellbeing.

Electroencephalography (EEG) signals are often contaminated with artifacts arising from many sources such as those with ocular and muscular origins. Artifact removal techniques often rely on the experience of the EEG technician to detect these artifact components for removal. This paper presents the results comparing an automated procedure (AT) against visually (VT) choosing artifactual components for removal, using second order blind identification (SOBI) and canonical correlation analyses. The results show that the resulting EEG signal after artifact removal for the AT and VT were comparable using a technique that measures the variance amongst electrodes and spectral energy. The AT technique is objective, faster and easier to use, and shown here to be comparable to the standard technique of visually detecting artifact components.

gyrus (rSTG & lSTG), and right inferior frontal gyrus (rIFG)) were analysed using dynamic causal modelling. Results: Bayesian model comparison showed that the five node model with modulation of all extrinsic and intrinsic connectivity between and within the cortical nodes was the most likely of all models. Model inversion showed that adolescents had reduced backward connection from rIFG to rSTG (p<0.04) together with increased excitatory activity at rSTG (p<0.02) with reduced modulation of intrinsic modulation at rSTG (p<0.02) and reduced forward connectivity from lA1 cortex to lSTG (p<0.03).

The paper proposes a multi-sensing system for the jointly assessment of electromyographic (EMG) and electroencephalographic (EEG) signals for the neuromuscular syndromes progression assessment, such as the Parkinson's disease (PD). The architecture implemented on Altera Cyclone V FPGA, interfaces 8 wireless surface EMGs (lower limbs) and 7 wireless EEGs (motor-cortex). The acquired signals, digitized and preprocessed, underwent a time-frequency features extraction (FE), making data suitable for a Serial Support Vector Machine (SSVM) real-time classification. The system has been in-vivo tested on 5 subjects (n=3 affected by mild PD and n=2 by severe PD). The experimental results showed an accuracy of ~94% in the pathology stage recognition, and near the 100% in distinguish healthy subjects from PD affected ones. The FPGA resources utilization results in: 31.04% ALMs, 15.87% registers, < 6% memory blocks and the 82.75% DSP blocks.

This paper describes the use of multi-way decomposition methods to efficiently summarize electroencephalographic (EEG) data. A space-frequency-time atomic decomposition was applied to EEG data recorded while subjects performed tasks associated with varying levels of cognitive performance. The new atomic decomposition of cognitive workload data revealed alpha and theta EEG oscillations which agree with observations reported in the brain research literature. The temporal signature of the atoms discriminates between different levels of cognitive activity. The results and analysis confirm the utility of the multi-way decomposition method to construct new models and algorithms for monitoring cognitive status, which can supplement or overcome existing approaches based on conventional two-dimensional space-time or frequency-time data decomposition.

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Electroencephalography (EEG) signals are often contaminated with artifacts arising from many sources such as those with ocular and muscular origins. Artifact removal techniques often rely on the experience of the EEG technician to detect these artifact components for removal. This paper presents the results comparing an automated procedure (AT) against visually (VT) choosing artifactual components for removal, using second order blind identification (SOBI) and canonical correlation analyses. The results show that the resulting EEG signal after artifact removal for the AT and VT were comparable using a technique that measures the variance amongst electrodes and spectral energy. The AT technique is objective, faster and easier to use, and shown here to be comparable to the standard technique of visually detecting artifact components.

Electroencephalography (EEG) alpha asymmetry is thought to reflect crucial brain processes underlying executive control, motivation, and affect. It has been widely used in psychopathology and, more recently, in novel neuromodulation studies. However, inconsistencies remain in the field due to the lack of consensus in methodological approaches employed and the recurrent use of small samples. Wearable technologies ease the collection of large and diversified EEG datasets that better reflect the general population, allow longitudinal monitoring of individuals, and facilitate realworld experience sampling. We tested the feasibility of using a low-cost wearable headset to collect a relatively large EEG database (N = 230, 22-80 years old, 64.3% female), and an open-source automatic method to preprocess it. We then examined associations between well-being levels and the alpha center of gravity (CoG) as well as trait EEG asymmetries, in the frontal and temporoparietal (TP) areas. Robust linear regression models did not reveal an association between well-being and alpha (8-13 Hz) asymmetry in the frontal regions, nor with the CoG. However, well-being was associated with alpha asymmetry in the TP areas (i.e., corresponding to relatively less left than right TP cortical activity as well-being levels increased). This effect was driven by oscillatory activity in lower alpha frequencies (8-10.5 Hz), reinforcing the importance of dissociating sub-components of the alpha band when investigating alpha asymmetries. Age was correlated with both well-being and alpha asymmetry scores, but gender was not. Finally, EEG asymmetries in the other frequency bands were not associated with well-being, supporting the specific role of alpha asymmetries with the brain mechanisms underlying well-being levels. Interpretations, limitations, and recommendations for future studies are discussed. This paper presents novel methodological, experimental, and theoretical findings that help advance human neurophysiological monitoring techniques using wearable neurotechnologies and increase the feasibility of their implementation into real-world applications.

Electrical source imaging is used in presurgical epilepsy evaluation and in cognitive neurosciences to localize neuronal sources of brain potentials recorded on EEG. This study evaluates the spatial accuracy of electrical source imaging for known sources, using electrical stimulation potentials recorded on simultaneous stereo-EEG and 37-electrode scalp EEG, and identifies factors determining the localization error. In 11 patients undergoing simultaneous stereo-EEG and 37-electrode scalp EEG recordings, sequential series of 99-110 biphasic pulses (2 ms pulse width) were applied by bipolar electrical stimulation on adjacent contacts of implanted stereo-EEG electrodes. The scalp EEG correlates of stimulation potentials were recorded with a sampling rate of 30 kHz. Electrical source imaging of averaged stimulation potentials was calculated utilizing a dipole source model of peak stimulation potentials based on individual fourcompartment finite element method head models with various skull conductivities (range from 0.0413 to 0.001 S/m). Fitted dipoles with a goodness of fit of ≥80% were included in the analysis. The localization error was calculated using the Euclidean distance between the estimated dipoles and the centre point of adjacent stimulating contacts. A total of 3619 stimulation locations, respectively, dipole localizations, were included in the evaluation. Mean localization errors ranged from 10.3 to 26 mm, depending on source depth and selected skull conductivity. The mean localization error increased with an increase in source depth (r(3617) = [0.19], P = 0.000) and decreased with an increase in skull conductivity (r(3617) = [−0.26], P = 0.000). High skull conductivities (0.0413-0.0118 S/m) yielded significantly lower localization errors for all source depths. For superficial sources (<20 mm from the inner skull), all skull conductivities yielded insignificantly different localization errors. However, for deeper sources, in particular >40 mm, high skull conductivities of 0.0413 and 0.0206 S/m yielded significantly lower localization errors. In relation to stimulation locations, the majority of estimated dipoles moved outward-forward-downward to inward-forward-downward with a decrease in source depth and an increase in skull conductivity. Multivariate analysis revealed that an increase in source depth, number of skull holes and white matter volume, while a decrease in skull conductivity independently led to higher localization error. This evaluation of electrical source imaging accuracy using artificial patterns with a high signal-to-noise ratio supports its application in presurgical epilepsy evaluation and cognitive neurosciences. In our artificial potential model, optimizing the selected skull conductivity minimized the localization error. Future studies should examine if this accounts for true neural signals.

Highlights  Novel multiscale modeling framework generates sleep spindles  Neural model embedded in cortex and cranial tissues reconstructed from MRI  Thalamocortical model of neural spindles linked to biophysical model of M/EEG  Simulated M/EEG spindles similar in amplitude and topography to empirical examples  Simulated spindles with more core-dominant cortical activity are more MEG weighted

In this paper, a novel relationship between instantaneous frequency (IF) and fundamental frequency (F0) in voiced parts of speech signals is presented. IF is calculated as the time-derivative of the phase of the analytic signal, yielding from Hilbert transform. Whereas F0 can be extracted using any classical pitch tracking technique (e.g. autocorrelation, cepstrum, subharmonic-to-harmonic ratio (SHR) independently of the tool used to extract F0. This relationship states that the envelope of the residual of the instantaneous frequency, defined as the difference between IF and the maximum of harmonics tends to F0. Such a direct relationship may be useful for further developments of F0 extraction directly from the speech signal, avoiding the approximation that exists in most pitch extraction techniques.

Food loss and waste has become a major social problem all over the world. To reduce "Food loss and waste," laws have been put in place to turn it into animal feed and fertilizers, relax delivery deadlines, and reduce serving sizes of meals. In addition, there are other activities promoted by the government, such as the use of food banks. However, even if all these activities are utilized, food loss and waste can never be reduced to zero. Another way to reduce food loss, other than these laws and activities, is to provide food materials to restaurants that provide inexpensive meals in the city, called "Kodomo-Shokudo." By using food that would otherwise be wasted at the "Kodomo-Shokudo," we can contribute to reducing food loss and lowering the operating costs of the these restaurants. Our aim is to clarify the difference between the food bank and "Kodomo-Shokudo" and create a route model that supplies food losses to the "Kodomo-Shokudo." Kodomo-Shokudo is a community place where local residents or NPOs take the initiative to provide meals to children for free or at a low cost. Consequently, Monte Carlo simulations show that the resulting transportation costs could be significantly reduced.

We present forward modeling solutions in the form of array response kernels for electroencephalography (EEG) and magnetoencephalography (MEG), assuming that a multilayer ellipsoidal geometry approximates the anatomy of the head and a dipole current models the source. The use of an ellipsoidal geometry is useful in cases for which incorporating the anisotropy of the head is important but a better model cannot be defined. The structure of our forward solutions facilitates the analysis of the inverse problem by factoring the lead field into a product of the current dipole source and a kernel containing the information corresponding to the head geometry and location of the source and sensors. This factorization allows the inverse problem to be approached as an explicit function of just the location parameters, which reduces the complexity of the estimation solution search. Our forward solutions have the potential of facilitating the solution of the inverse problem, as they provide algebraic representations suitable for numerical implementation. The applicability of our models is illustrated with numerical examples on real EEG/MEG data of N20 responses. Our results show that the residual data after modeling the N20 response using a dipole for the source and an ellipsoidal geometry for the head is in average lower than the residual remaining when a spherical geometry is used for the same estimated dipole.

In the present exploratory study based on 7 subjects, we examined the composition of magnetoencephalographic (MEG) brain oscillations induced by the presentation of an auditory, visual, and audiovisual stimulus (a talking face) using an oddball paradigm. The composition of brain oscillations were assessed here by analyzing the probability-classification of short-term MEG spectral patterns. The probability index for particular brain oscillations being elicited was dependent on the type and the modality of the sensory percept. The maintenance of the integrated audiovisual percept was accompanied by the unique composition of distributed brain oscillations typical of auditory and visual modality, and the contribution of brain oscillations characteristic for visual modality was dominant. Oscillations around 20 Hz were characteristic for the maintenance of integrated audiovisual percept. Identifying the actual composition of brain oscillations allowed us (a) to distinguish two subjectively/consciously identical mental percepts, and (b) to characterize the types of brain functions involved in the maintenance of the multi-sensory percept.

In this paper we propose a method for the automatic detection of hemangioma regions, consisting of a cascade of algorithms: a Self Organizing Map (SOM) for clustering the image pixels in 25 classes (using a 5x5 output layer) followed by a morphological method of reducing the number of classes (MMRNC) to only two classes: hemangioma and non-hemangioma. We named this method SOM-MMRNC. To evaluate the performance of the proposed method we have used Fuzzy C-means (FCM) for comparison. The algorithms were tested on 33 images; for most images, the proposed method and FCM obtain similar overall scores, within one percent of each other. However, in about 18% of the cases, there is a significant increase of the overall score for SOM-MMRNC (over 3.5%). On average, the results obtained with the proposed cascade are 1.06% better for each image.

The processing of visual motion was tested by means of event related potentials recording (ERP) using a paradigm designed to produce a visual mismatch negativity eVect. The stimuli were unattended and presented in the peripheral visual Weld (outside the central 15°). The standard stimulus consisted of an up/down motion sequence, whilst the deviant stimulus of a down/up motion sequence. SigniWcant ERP diVerences between the standard and deviant conditions were found in 8 out of 10 adult subjects already in 80 ms and prevailingly in interval 145-260 ms from the initial stimulus presentation. The results demonstrate that the magnocellular information undergoes processing capable of detecting diVerences in the sequence of unattended peripheral motion stimuli.

This paper presents an extensive review on understanding all types of EEG artifacts which are incorporated in EEG signals while taking measurements from scalp of the different subjects. Artifacts which are more prominent and occurred very often are 'Ocular artifacts'. The characteristics of an EEG signal having OA's are mentioned and explained in the paper. Ocular artifacts have a large impact on measurement. Reasons for inclusion of these artifacts and why the elimination is essential have been explained in this paper. Artifacts can be also named as noise; and noise has to be removed from the signal. For removing the OA's signal modeling is essential. Many Artifact correction approaches have been developed to provide reliable results of identification detection and removal. The different methods like ICA, WT, ANN etc and their characteristics which are explained by the researchers is the main review part of this paper which will give us an idea of building a more accurate system algorithm to remove ocular artifacts from the contaminated EEG signal. In This paper, the different methods and their result parameters along with their values have been given in the tabular form.

This research aims to develop a driver drowsiness monitoring system by analyzing the electroencephalographic (EEG) signals in a software scripted environment and using a driving simulator. These signals are captured by a multi-channel electrode system. Any muscle movement impacts the EEG signal recording which translates to artifacts. Therefore, noise from the recording is eliminated by subtracting the noisy signal from the original EEG recording. The actual EEG signals are then subjected to band pass filtering with cutoff frequencies 0.5 Hz and 100 Hz. The filtered signals are analyzed using a time-frequency technique known as the Discrete Wavelet Transform (DWT). A third order Debauchies' wavelet and five level decomposition is utilized to segregate the signal into five sub-bands, namely, delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz) and gamma (> 30 Hz). First order statistical moments such as mean, median, variance, standard deviation and mode of the sub-bands are calculated and stored as features. These features serve as an input to the next stage of system classification. Unsupervised learning through K-means clustering is employed since the classes of the signals are unknown. This provides a strong decision making tool for a real-time drowsiness detection system. The algorithm developed in this work has been tested on twelve samples from the Physionet sleep-EDF database.

The study of the neural mechanisms underlying sports performance was aimed at studying the characteristics of the brain electrical activity of athletesfighters in comparison with non-athletes. Thirty young men of 19.97±0.23 years old (with the height of 179.82±2.15 cm and the body weight of 78.61±1.68 kg) engaged in wrestling (with the qualification "Master of Sports"), and thirty young men of 19.25±0.18 years old (with the height of 178.35±2.25 cm and the body weight of 77.93±1.45 kg), not involved in sports, analyzed the frequency ranges delta, theta, alpha, beta 1 and beta 2 rhythms of electroencephalography (EEG) at rest in three epochs: the first epoch with closed eyes, the second epoch with open eyes, and the third epoch with closed eyes. The results of the study showed that in wrestlers, the neural advantage over non-athletes is expressed only in a decrease in alpha rhythm fluctuations: with closed eyes in the left hemisphere by 17.07% (p<0.05), and in the right hemisphere by 17.35% (p<0.05). With open eyes, the difference was 21.17% (p<0.01) in the left hemisphere and 19.27% (p<0.05) in the right hemisphere, which indicates a lower cortical activity of the brain of athletes. At the same time, the coefficient of reactivity (CR) of the alpha rhythm during the receipt of visual information in athletes in the left hemisphere is 20% higher, and when it is interrupted, it is 18% lower than in non-athletes. In the right hemisphere, CR does not differ. We concluded that wrestlers recruited fewer neural resources without compromising performance, supporting the hypothesis of neuronal flexibility in the brains of athletes compared to non-athletes.

Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to accurately depict seizure-onset location. Methods: A blinded investigation used 23 EEG recordings of seizures from 8 patients. Each recording was uninterpretable with digital filtering because of muscle artifact and processed using AR1 and AR2 and reviewed by 26 EEG specialists. EEG readers assessed seizure-onset time, lateralization, and region, and specified confidence for each determination. The two methods were validated on the basis of the number of readers able to render assignments, confidence, the intra-class correlation (ICC), and agreement with other clinical findings. Results: Among the 23 seizures, two-thirds of the readers were able to delineate seizure-onset time in 10 of 23 using AR1, and 15 of 23 using AR2 (p&lt;0.01)....

The presentation of simple auditory stimuli can significantly impact visual processing and even induce visual illusions, such as the auditory-induced double flash illusion (DFI). These cross-modal processes have been shown to be driven by occipital oscillatory activity within the alpha band. Whether this phenomenon is network specific or can be generalized to other sensory interactions remains unknown. The aim of the current study was to test whether cross-modal interactions between somatosensory-to-visual areas leading to the same (but tactile-induced) DFI share similar properties with the auditory DFI. We hypothesized that if the effects are mediated by the oscillatory properties of early visual areas per se, then the two versions of the illusion should be subtended by the same neurophysiological mechanism (i.e., the speed of the alpha frequency). Alternatively, if the oscillatory activity in visual areas predicting this phenomenon is dependent on the specific neural network involved, then it should reflect network-specific oscillatory properties. In line with the latter, results recorded in humans (both sexes) show a network-specific oscillatory profile linking the auditory DFI to occipital alpha oscillations, replicating previous findings, and tactile DFI to occipital beta oscillations, a rhythm typical of somatosensory processes. These frequency-specific effects are observed for visual (but not auditory or somatosensory) areas and account for auditory-visual connectivity in the alpha band and somatosensory-visual connectivity in the beta band. We conclude that task-dependent visual oscillations reflect network-specific oscillatory properties favoring optimal directional neural communication timing for sensory binding.

Brahma Kumaris Rajayoga’s open eyed ‘seed stage meditation’ was studied in 52 meditators. Meditation stages: concentration on peace, realization of being a soul and communion with the Supreme Soul. Frequency domain brain electric source localization was used on multichannel EEG recordings to establish activation differences between meditation and open eyed, task-free resting. Additional exploratory analyses probed for effects of passing time from initial rest through meditation to final rest. Meditation showed reduced activity in delta and increased activity in low alpha frequencies. Affected brain areas comprised the following networks: central executive network, mirroring network, task-positive and task-negative network. These altered activations reflect the main cognitive-affective and behavioral specifics of seed stage meditation: attention modulation, self-related processing, experiencing the soul as a point of light between the eyes, endowing the soul with the properties (peac...

Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to accurately depict seizure-onset location. Methods: A blinded investigation used 23 EEG recordings of seizures from 8 patients. Each recording was uninterpretable with digital filtering because of muscle artifact and processed using AR1 and AR2 and reviewed by 26 EEG specialists. EEG readers assessed seizure-onset time, lateralization, and region, and specified confidence for each determination. The two methods were validated on the basis of the number of readers able to render assignments, confidence, the intra-class correlation (ICC), and agreement with other clinical findings. Results: Among the 23 seizures, two-thirds of the readers were able to delineate seizure-onset time in 10 of 23 using AR1, and 15 of 23 using AR2 (p&lt;0.01)....

Multisensory integration is quintessential to adaptive behavior, with clinical populations showing significant impairments in this domain, most notably hallucinatory reports. Interestingly, altered cross-modal interactions have also been reported in healthy individuals when engaged in tasks such as the Sound-Induced Flash-Illusion (SIFI). The temporal dynamics of the SIFI have been recently tied to the speed of occipital alpha rhythms (IAF), with faster oscillations entailing reduced temporal windows within which the illusion is experienced. In this regard, entrainment-based protocols have not yet implemented rhythmic transcranial magnetic stimulation (rhTMS) to causally test for this relationship. It thus remains to be evaluated whether rhTMS-induced acoustic and somatosensory sensations may not specifically interfere with the illusion. Here, we addressed this issue by asking 27 volunteers to perform a SIFI paradigm under different Sham and active rhTMS protocols, delivered over th...

In this paper, a new fuzzy adaptive neurofeedback training procedure (FNFT) is proposed, in which a more effective performance in neurofeedback training can be expected. In the proposed FNFT, the threshold is adaptively set considering the cortical activity of the subject. Scoring index (SI) (the number of points increased in subject's score) is set according to the brain activity of the subject and is calculated using a fuzzy rule based system. When training feature surpasses the threshold, the SI points are then added to the points of the subject. This adaptive scoring index leads to having an efficient indicator for the success rate of the subject. In addition, the subject is rewarded with an audio or visual feedback. The sound intensity of the audio feedback and the length and width of the video frame are adjusted in accordance with the SI. Finally, an EEG feature is also considered (brain mental fatigue index) to stop the training as the subject becomes mentally fatigued.

Electroencephalography (EEG) signals are often contaminated with artifacts arising from many sources such as those with ocular and muscular origins. Artifact removal techniques often rely on the experience of the EEG technician to detect these artifact components for removal. This paper presents the results comparing an automated procedure (AT) against visually (VT) choosing artifactual components for removal, using second order blind identification (SOBI) and canonical correlation analyses. The results show that the resulting EEG signal after artifact removal for the AT and VT were comparable using a technique that measures the variance amongst electrodes and spectral energy. The AT technique is objective, faster and easier to use, and shown here to be comparable to the standard technique of visually detecting artifact components.

An electroencephalogram (EEG) is a medical examination that records the electrical activity of the human brain. In order to record these signals, electrodes are placed on the scalp, and these electrodes detect any activity of the brain cells. This result is used to diagnose patients with epilepsy, sleep disorders, brain tumors, seizure disorders, and brain disorders. However, these signals are often corrupted by various artefacts either generated from the human body(physiological) or the electrical apparatus used(non-physiological). The removal of these artefacts is vital as their presence may result in the wrong diagnosis of a disease. One of these artefacts is generated because of various kinds of eye movements, i.e. EOG. In this paper, we will be focusing on how ocular artefacts occur and how they affect the EEG signal. Later we will look into different types of artefact removal techniques and how they perform differently from each other on the basis of different factors such as signal-to-noise-ratio etc. The aim of this study is to get a detailed understanding of various ocular artefact removal techniques, challenges related to them and finding an efficient way to obtain an uncorrupted EEG signal.

The dynamic structure of the EEG signal is characterized by segments of synchronization and desynchronization. In this paper, the frequency and duration of these segments were monitored during calm meditation and insight meditation in experienced and naive meditators. A newly developed methodology based on complex continuous wavelet coherence was used to estimate these parameters. The durations highly depend on frequency band and vary from 60 ms to 250 ms. A shorter duration and a lower frequency of synchronization were found for experienced meditators during both types of meditations for the real and the imaginary parts of the complex continuous wavelet coherence. The greatest duration differences were in the gamma band, which may be associated with handling attention during meditation, whereas the differences in the alpha band were most significant for frequency. Combining the two parameters resulted in the total duration of the synchronization, which has discriminative accuracy of up to 100% and appears to be a sensitive parameter of the length of training of meditators. Keywords Calm meditation Á Continuous complex wavelet coherence Á EEG synchronization Á Insight meditation Abbreviations CWT Continuous wavelet transform WCS Wavelet cross spectrum CCWC Complex continuous wavelet coherence ROC Receiver operating characteristic EEG Electroencephalography FIR Finite impulse response Electronic supplementary material The online version of this article (

Mild to severe traumatic brain injuries have lasting effects on everyday functioning. Issues relating to sensory problems are often overlooked or not addressed until well after the onset of the injury. In particular, vision problems related to ambient vision and the magnocellular pathway often result in posttrauma vision syndrome or visual midline shift syndrome. Symptoms from these syndromes are not restricted to the visual domain. Patients commonly experience proprioceptive, kinesthetic, vestibular, cognitive, and language problems. Neurooptometric rehabilitation often entails the use of corrective lenses, prisms, and binasal occlusion to accommodate the unstable magnocellular system. However, little is known regarding the neural mechanisms engaged during neurooptometric rehabilitation, nor how these mechanisms impact other domains. Event-related potentials from noninvasive electrophysiological recordings can be used to assess rehabilitation progress in patients. In this case report, high-density visual event-related potentials were recorded from one patient with posttrauma vision syndrome and secondary visual midline shift syndrome during a pattern reversal task, both with and without prisms. Results indicate that two factors occurring during the end portion of the P148 component (168-256 milliseconds poststimulus onset) map onto two separate neural systems that were engaged with and without neurooptometric rehabilitation. Without prisms, neural sources within somatosensory, language, and executive brain regions engage inefficient magnocellular system processing. However, when corrective prisms were worn, primary visual areas were appropriately engaged. The impact of using early neurooptometric rehabilitation for posttrauma vision syndrome, visual midline shift syndrome, and other similar subtle vision disorders to support neural reorganization is discussed.

The present paper envisages the application of mathematical modelling with the autoregressive (AR) model method as a tool to analyze electroencephalogram data in rat subjects of transient focal cerebral ischemia. This modelling method was used to determine the frequencies and characteristic changes in brain waveforms which occur as a result of disorders or fluctuating physiological states. This method of analysis was utilized to ensure actual correlation of the different mathematical paradigms. The EEG data was obtained from different regions of the rat brain and was modelled by AR method in a MATLAB platform. AR modelling was utilized to study the long-term functional outcomes of a stroke and also is preferable for EEG signal analysis because the signals consist of discrete frequency intervals. Modern spectral analysis, namely AR spectrum analysis, was used to correlate the conditional and prevalent changes in brain function in response to a stroke.

EOG is one of the major artifacts in EEG signal processing. There are varieties of methods have been proposed that aim to eliminate the influence of Occular artifacts on the EEG signals. However, the problem is the trade-off between their performance of removing EOG artifact and their simplicity. In this study, we propose a simple and reliable method but giving a good performance. The idea of this method is to use a multi-threshold technique to target EOG contaminated parts in the signal then selectively subtract it out in order to get a corrected signal with a minimum alteration on the uncontaminated parts. In this study, we used triple-threshold, both in time and frequency domain, to target the contaminated parts (or EOG artifact component). The result shows that besides its simplicity, this method also reliable and effective when selectively removed some typical EOG artifacts like blinks or eye movements without altering other clean parts in the EEG signals. More than that, our method is also able to extract the estimated EOG artifact component from the EEG signal. The need for this method is only one single prefrontal EEG channel, no need for an EOG reference channel for the input. The source code of this method is freely available to download in the form of a MATLAB function by request. We encourage the researchers to give it a try.

In functional neuroimaging, a crucial problem is to localize active sources within the brain non-invasively, from the knowledge of the electromagnetic measurements outside the head. Identication of point sources from boundary measurements is an ill-posed inverse problem. In the case of electroencephalography (EEG), measurements are only available at electrode positions, the number of sources is not known in advance, and the medium within the head is inhomogeneous. This paper presents a new method for EEG source localization, based on rational approximation techniques in the complex plane. The method is used in the context of a nested sphere head model, in combination with a cortical mapping procedure. Results on simulated data prove the applicability of the method in the context of realistic measurements congurations.

Several studies have reported differences in long-range temporal correlations of EEG oscillations between depressed and nondepressed individuals. The question remains unsolved whether these differences are also linked to negative emotion regulation strategies that configure a depressive style. In this study we applied detrended fluctuation analysis to the amplitude envelope of broad band and narrow band (theta and alpha) spontaneous EEG oscillations of a sample (N = 56) of young nondepressed adults to whom several emotion regulation and depression questionnaires were administered. Linear positive correlations between the scaling exponents of both broad band and theta band oscillations and negative emotion regulation strategies and depression scores were found. These results suggest that previously found differences between depressed and nondepressed individuals may exist before depression manifests, as differences could be linked to a negative emotion regulation style that in some c...