scholarly journals Causal Shannon–Fisher Characterization of Motor/Imagery Movements in EEG

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 660 ◽  
Author(s):  
Román Baravalle ◽  
Osvaldo Rosso ◽  
Fernando Montani
Keyword(s):  
Large Scale ◽  
Rhythmic Activity ◽  
Salient Feature ◽  
Neural Oscillations ◽  
Beta Band ◽  
Monitoring Method ◽  
Brain Functions ◽  
Electroencephalogram Eeg ◽  
The Brain

The electroencephalogram (EEG) is an electrophysiological monitoring method that allows us to glimpse the electrical activity of the brain. Neural oscillations patterns are perhaps the best salient feature of EEG as they are rhythmic activities of the brain that can be generated by interactions across neurons. Large-scale oscillations can be measured by EEG as the different oscillation patterns reflected within the different frequency bands, and can provide us with new insights into brain functions. In order to understand how information about the rhythmic activity of the brain during visuomotor/imagined cognitive tasks is encoded in the brain we precisely quantify the different features of the oscillatory patterns considering the Shannon–Fisher plane H × F . This allows us to distinguish the dynamics of rhythmic activities of the brain showing that the Beta band facilitate information transmission during visuomotor/imagined tasks.

Implantable neural interfaces

2018 ◽  
Author(s):  
Stefano Vassanelli
Keyword(s):  
Brain Function ◽  
Large Scale ◽  
Ionic Channels ◽  
Patch Clamp Technique ◽  
Advantages And Disadvantages ◽  
Optogenetic Stimulation ◽  
Physical Interfaces ◽  
The Brain

Establishing direct communication with the brain through physical interfaces is a fundamental strategy to investigate brain function. Starting with the patch-clamp technique in the seventies, neuroscience has moved from detailed characterization of ionic channels to the analysis of single neurons and, more recently, microcircuits in brain neuronal networks. Development of new biohybrid probes with electrodes for recording and stimulating neurons in the living animal is a natural consequence of this trend. The recent introduction of optogenetic stimulation and advanced high-resolution large-scale electrical recording approaches demonstrates this need. Brain implants for real-time neurophysiology are also opening new avenues for neuroprosthetics to restore brain function after injury or in neurological disorders. This chapter provides an overview on existing and emergent neurophysiology technologies with particular focus on those intended to interface neuronal microcircuits in vivo. Chemical, electrical, and optogenetic-based interfaces are presented, with an analysis of advantages and disadvantages of the different technical approaches.

scholarly journals High-frequency oscillations and the neurobiology of schizophrenia

2013 ◽  
Vol 15 (3) ◽  
pp. 301-313 ◽  
Keyword(s):  
High Frequency ◽  
Large Scale ◽  
Oscillatory Activity ◽  
Current Evidence ◽  
Neural Oscillations ◽  
Future Research ◽  
Normal Brain ◽  
Brain Functions ◽  
Critical Issues ◽  
Underlying Mechanisms

Neural oscillations at low- and high-frequency ranges are a fundamental feature of large-scale networks. Recent evidence has indicated that schizophrenia is associated with abnormal amplitude and synchrony of oscillatory activity, in particular, at high (beta/gamma) frequencies. These abnormalities are observed during task-related and spontaneous neuronal activity which may be important for understanding the pathophysiology of the syndrome. In this paper, we shall review the current evidence for impaired beta/gamma-band oscillations and their involvement in cognitive functions and certain symptoms of the disorder. In the first part, we will provide an update on neural oscillations during normal brain functions and discuss underlying mechanisms. This will be followed by a review of studies that have examined high-frequency oscillatory activity in schizophrenia and discuss evidence that relates abnormalities of oscillatory activity to disturbed excitatory/inhibitory (E/I) balance. Finally, we shall identify critical issues for future research in this area.

scholarly journals Quantification of EEG Brain Activity in the Self-Paced Regime

2018 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Joana Silva ◽  
A. Martins Da Silva ◽  
Luís Coelho
Keyword(s):  
Brain Activity ◽  
Rhythmic Activity ◽  
The Self ◽  
Automatic Quantification ◽  
Frequency Bands ◽  
Cerebral Activity ◽  
Cognitive Information ◽  
Specific Frequency ◽  
Electroencephalogram Eeg ◽  
The Brain

The processing of motor, sensory and cognitive information by the brain can result in changes of the electroencephalogram (EEG) by Event Related Desynchronization (ERD) or Event Related Synchronization (ERS). The first one concerns a decrease in the amplitude of a rhythmic activity while the second corresponds to its increase. The analysis of these two phenomena in specific frequency bands - alpha (8-13 Hz) and beta (14-30 Hz) - allows the understanding of the cerebral activity. This study focuses on the quantification of cerebral activity by determining the ERD and ERS on the referred band, induced by self-paced movements, by using EEGLAB and MATLAB tools. This was achieved by the creation of a new and automatic quantification algorithm. The results indicate that a greater desynchronization of the signal is accompanied by a decrease in the amplitude of the same. As a conclusion, the cerebral activity varies in terms of synchronization and desynchronization among certain frequency bands in several zones, according to the tasks performed.

scholarly journals Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision-making

2017 ◽  
Vol 1 (2) ◽  
pp. 166-191 ◽  
Author(s):  
Mohsen Alavash ◽  
Christoph Daube ◽  
Malte Wöstmann ◽  
Alex Brandmeyer ◽  
Jonas Obleser
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Network Dynamics ◽  
Neural Oscillations ◽  
Perceptual Decision Making ◽  
Beta Band ◽  
Perceptual Decision ◽  
Large Scale Network ◽  
Scale Network ◽  
Decision Speed

Perceptual decisions vary in the speed at which we make them. Evidence suggests that translating sensory information into perceptual decisions relies on distributed interacting neural populations, with decision speed hinging on power modulations of the neural oscillations. Yet the dependence of perceptual decisions on the large-scale network organization of coupled neural oscillations has remained elusive. We measured magnetoencephalographic signals in human listeners who judged acoustic stimuli composed of carefully titrated clouds of tone sweeps. These stimuli were used in two task contexts, in which the participants judged the overall pitch or direction of the tone sweeps. We traced the large-scale network dynamics of the source-projected neural oscillations on a trial-by-trial basis using power-envelope correlations and graph-theoretical network discovery. In both tasks, faster decisions were predicted by higher segregation and lower integration of coupled beta-band (∼16–28 Hz) oscillations. We also uncovered the brain network states that promoted faster decisions in either lower-order auditory or higher-order control brain areas. Specifically, decision speed in judging the tone sweep direction critically relied on the nodal network configurations of anterior temporal, cingulate, and middle frontal cortices. Our findings suggest that global network communication during perceptual decision-making is implemented in the human brain by large-scale couplings between beta-band neural oscillations.

Brain death: understanding the organism as a whole

1999 ◽  
Vol 48 (3) ◽  
pp. 497-515
Author(s):  
J. Bonelli ◽  
E.H. Prat ◽  
N. Auner ◽  
R. Bonelli
Keyword(s):  
Dead Body ◽  
Life And Death ◽  
Brain Functions ◽  
The Status ◽  
Brain Dead ◽  
The Moment ◽  
Definition Of ◽  
The Brain ◽  
Living Being

Since intensive care medicine enables us to maintain blood circulation and respiration artificially for some time, the usual criteria for death, such as cardiac arrest and cessation of respiration, are not applicable in all cases. Thus, the irreversible breakdown of the brain functions have come to be accepted as the most prominent factor for the occurence of death. This criterion is linked primarily to the disintegration of the organism as a whole. Yet the controversy surrounding the moment when a man can be declared dead or alive has not yet been resolved. The decisive weak point in this controversial discussion seems to be that the notion of the “organism as a whole” is inadequately defined. The aim of this study is to fill this void. Thus, at a first approximation, a rough definition of the “organism as a whole” is given. In a second step we turn to examine the empirical evidence related to the question of whether this attribute is possessed or not by a human body with an irreparably damaged brain. For the characterization of life and death, as related to our question, it is important to distingnish between derivated biological life (isolatedly living cells or organs, cell cultures, heart-lung-compound) and a living being. For this distinction the criteria of completion, indivisibility, auto-finality and identity have been considered. If these are missing a living being does not exist. Then a man is no longer a living man, he is dead. In brain-dead body one finds a number of signs of life such as heartbeat, metabolism, growth of cells, regeneration an so forth. These signs of life, however, are not signs of an organsim as a whole but signs of a physiological combination of organs whose parts - directed from the outside - are dependent on each other. The brain-dead body lacks, however, the four criteria of a living being. Thus it is no longer a living man. It is a purely derivated biological life. If we regard the brain-dead body indirectly by considering the status of the brain itself and its functions we can say that the brain is the constitutive foundation (the guarantor) for the identity and completion of an individual as a whole. With the loss of the brain this wholeness is lost. The man is dead.

Basic Circuit Design of a Neural Processor: Analog CMOS Implementation of Spiking Neurons and Dynamic Synapses

2003 ◽  
Vol 15 (2) ◽  
pp. 208-218 ◽  
Author(s):  
Yusuke Kanazawa ◽  
◽  
Tetsuya Asai ◽  
Yoshihito Amemiya
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Spiking Neurons ◽  
Oxide Semiconductor ◽  
Competitive Neural Network ◽  
Brain Functions ◽  
Analog Cmos ◽  
Integration Architecture ◽  
Dynamic Synapses ◽  
The Brain

We discuss the integration architecture of spiking neurons, predicted to be next-generation basic circuits of neural processor and dynamic synapse circuits. A key to development of a brain-like processor is to learn from the brain. Learning from the brain, we try to develop circuits implementing neuron and synapse functions while enabling large-scale integration, so large-scale integrated circuits (LSIs) realize functional behavior of neural networks. With such VLSI, we try to construct a large-scale neural network on a single semiconductor chip. With circuit integration now reaching micron levels, however, problems have arisen in dispersion of device performance in analog IC and in the influence of electromagnetic noise. A genuine brain computer should solve such problems on the network level rather than the element level. To achieve such a target, we must develop an architecture that learns brain functions sufficiently and works correctly even in a noisy environment. As the first step, we propose an analog circuit architecture of spiking neurons and dynamic synapses representing the model of artificial neurons and synapses in a form closer to that of the brain. With the proposed circuit, the model of neurons and synapses can be integrated on a silicon chip with metal-oxide-semiconductor (MOS) devices. In the sections that follow, we discuss the dynamic performance of the proposed circuit by using a circuit simulator, HSPICE. As examples of networks using these circuits, we introduce a competitive neural network and an active pattern recognition network by extracting firing frequency information from input information. We also show simulation results of the operation of networks constructed with the proposed circuits.

scholarly journals Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Yogendra Narain Singh ◽  
Sanjay Kumar Singh ◽  
Amit Kumar Ray
Keyword(s):  
Cardiac Rhythm ◽  
Large Scale ◽  
Eeg Signals ◽  
Identity Verification ◽  
Test Conditions ◽  
Signal Features ◽  
Inherent Feature ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg ◽  
The Brain

This paper presents the effectiveness of bioelectrical signals such as the electrocardiogram (ECG) and the electroencephalogram (EEG) for biometric applications. Studies show that the impulses of cardiac rhythm and electrical activity of the brain recorded in ECG and EEG, respectively; have unique features among individuals, therefore they can be suggested to be used as biometrics for identity verification. The favourable characteristics to use the ECG or EEG signals as biometric include universality, measurability, uniqueness and robustness. In addition, they have the inherent feature of vitality that signifies the life signs offering a strong protection against spoof attacks. Unlike conventional biometrics, the ECG or EEG is highly confidential and secure to an individual which is difficult to be forged. We present a review of methods used for the ECG and EEG as biometrics for individual authentication and compare their performance on the datasets and test conditions they have used. We illustrate the challenges involved in using the ECG or EEG as biometric primarily due to the presence of drastic acquisition variations and the lack of standardization of signal features. In order to determine the large-scale performance, individuality of the ECG or EEG is another challenge that remains to be addressed.

Imagined Temporal Groupings Tune Oscillatory Neural Activity for Processing Rhythmic Sounds

2015 ◽  
Vol 3 (1-2) ◽  
pp. 172-188
Author(s):  
Brandon T. Paul ◽  
Per B. Sederberg ◽  
Lawrence L. Feth
Keyword(s):  
Multivariate Analysis ◽  
Neural Activity ◽  
Neural Oscillations ◽  
Neural Activation ◽  
Focus Attention ◽  
Complex Sound ◽  
Frequency Bands ◽  
Hierarchical Timing ◽  
Electroencephalogram Eeg ◽  
The Brain

Temporal patterns within complex sound signals, such as music, are not merely processed after they are heard. We also focus attention to upcoming points in time to aid perception, contingent upon regularities we perceive in the sounds’ inherent rhythms. Such organized predictions are endogenously maintained as meter — the patterning of sounds into hierarchical timing levels that manifest as strong and weak events. Models of neural oscillations provide potential means for how meter could arise in the brain, but little evidence of dynamic neural activity has been offered. To this end, we conducted a study instructing participants to imagine two-based or three-based metric patterns over identical, equally-spaced sounds while we recorded the electroencephalogram (EEG). In the three-based metric pattern, multivariate analysis of the EEG showed contrasting patterns of neural oscillations between strong and weak events in the delta (2–4 Hz) and alpha (9–14 Hz), frequency bands, while theta (4–9 Hz) and beta (16–24 Hz) bands contrasted two hierarchically weaker events. In two-based metric patterns, neural activity did not drastically differ between strong and weak events. We suggest the findings reflect patterns of neural activation and suppression responsible for shaping perception through time.

scholarly journals Variability and stability of large-scale cortical oscillation patterns

2016 ◽  
Author(s):  
Roy Cox ◽  
Anna C Schapiro ◽  
Robert Stickgold
Keyword(s):  
Large Scale ◽  
Rhythmic Activity ◽  
Brain Organization ◽  
Spatial And Temporal Scales ◽  
Network Analyses ◽  
Performance Results ◽  
And Behavior

AbstractIndividual differences in brain organization exist at many spatial and temporal scales, contributing to the substantial heterogeneity underlying human thought and behavior. Oscillatory neural activity is crucial for these behaviors, but how such rhythms are expressed across the cortex within and across individuals has not been thoroughly characterized. Combining electroencephalography (EEG) with representational similarity and multivariate classification techniques, we provide a systematic characterization of brain-wide activity across frequency bands and oscillatory features during rest and task performance. Results indicate that oscillatory profiles exhibit sizable group-level correspondences, indicating the presence of common templates of oscillatory organization. At the same time, we observed well-defined subject-specific network profiles that were discernible above and beyond the structure shared across individuals. These individualized patterns were sufficiently stable over time to allow successful classification of individuals several months later. Finally, our findings indicate that the network structure of rhythmic activity varies considerably across distinct oscillatory frequencies and features, suggesting the existence of multiple, parallel information processing streams embedded in distributed electrophysiological activity. Together, these findings affirm the richness of spatiotemporal EEG signals and emphasize the utility of multivariate network analyses for understanding the role of brain oscillations in physiology and behavior.

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