Brain Rhythms

2017 ◽  
pp. 165-188
Author(s):  
Riitta Hari ◽  
Aina Puce
Keyword(s):  
Auditory Cortex ◽  
Eeg Signal ◽  
Gamma Delta ◽  
Brain Rhythms ◽  
Slow Oscillations ◽  
Timing Information ◽  
Sensory Motor ◽  
Signal Changes ◽  
Eeg Recordings ◽  
The Brain

This chapter provides examples of studies of MEG and EEG brain rhythms that have considerably increased understanding of the human brain’s sensory, motor, cognitive, and affective functions. Parieto-occipital alpha, Rolandic mu and auditory-cortex tau rhythms, as well as more widely spread beta, theta, gamma, delta and ultra-slow oscillations are described. Additionally, MEG/EEG signal changes that accompany different vigilance states, such as drowsiness and sleep, as well as anesthesia, are discussed. We emphasize the importance of timing information that MEG and EEG recordings, including the brain rhythms, provide. In this and subsequent chapters, we rely somewhat on our own studies and experiences, so as to give educational insights from the pitfalls and challenges we ourselves have experienced.

scholarly journals Changes in EEG permutation entropy in the evening and in the transition from wake to sleep

SLEEP ◽  
2020 ◽  
Author(s):  
Fengzhen Hou ◽  
Lulu Zhang ◽  
Baokun Qin ◽  
Giulia Gaggioni ◽  
Xinyu Liu ◽  
...  
Keyword(s):  
Sleep Onset ◽  
Sleep Time ◽  
Large Datasets ◽  
Permutation Entropy ◽  
Brain State ◽  
Eeg Signal ◽  
Spectral Measures ◽  
Progressive Change ◽  
Eeg Recordings ◽  
The Brain

Abstract Quantifying the complexity of the EEG signal during prolonged wakefulness and during sleep is gaining interest as an additional mean to characterize the mechanisms associated with sleep and wakefulness regulation. Here, we characterized how EEG complexity, as indexed by Multiscale Permutation Entropy (MSPE), changed progressively in the evening prior to light off and during the transition from wakefulness to sleep. We further explored whether MSPE was able to discriminate between wakefulness and sleep around sleep onset and whether MSPE changes were correlated with spectral measures of the EEG related to sleep need during concomitant wakefulness (theta power—Ptheta: 4–8 Hz). To address these questions, we took advantage of large datasets of several hundred of ambulatory EEG recordings of individual of both sexes aged 25–101 years. Results show that MSPE significantly decreases before light off (i.e. before sleep time) and in the transition from wakefulness to sleep onset. Furthermore, MSPE allows for an excellent discrimination between pre-sleep wakefulness and early sleep. Finally, we show that MSPE is correlated with concomitant Ptheta. Yet, the direction of the latter correlation changed from before light-off to the transition to sleep. Given the association between EEG complexity and consciousness, MSPE may track efficiently putative changes in consciousness preceding sleep onset. An MSPE stands as a comprehensive measure that is not limited to a given frequency band and reflects a progressive change brain state associated with sleep and wakefulness regulation. It may be an effective mean to detect when the brain is in a state close to sleep onset.

scholarly journals Epileptic Seizures Prediction Using Deep Learning Methods on EEG Recordings

Webology ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 717-734
Author(s):  
G. Maragatham ◽  
T. Kirthiga Devi ◽  
P. Savaridassan ◽  
Sachin Garg
Keyword(s):  
Brain Activity ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Severe Damage ◽  
Unusual Behavior ◽  
Appropriate Care ◽  
Eeg Recordings ◽  
American Society ◽  
The Brain ◽  
Abnormal Brain

Epilepsy is a neurological disorder that disturbs the brain and causes abnormal brain activity. It results in loss of awareness in some cases and unusual behavior and sensations. In this regard, if the seizures could be identified in its earlier stages then the patient can be provided appropriate care and treatment in time and prevent any severe damage to the patient as a whole. In this paper, we try to detect epilepsy using the EEG Signal Recordings and classify them using pre-trained CNN models between preictal and interictal classes. For this we are advocating the use of American Society for Epilepsy Dataset. The focus is on detecting the epilepsy pattern from the EEG recordings in a timely and accurate manner.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

scholarly journals Conditional Entropy: A Potential Digital Marker for Stress

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 286
Author(s):  
Soheil Keshmiri
Keyword(s):  
Resting State ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Solution Concept ◽  
Conditional Entropy ◽  
K Nearest Neighbor ◽  
Brain Response ◽  
Substantial Progress ◽  
Eeg Recordings ◽  
The Brain

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

Central Auditory Processing

2021 ◽  
Author(s):  
Josef P. Rauschecker
Keyword(s):  
Auditory Cortex ◽  
Auditory Processing ◽  
Cognitive Abilities ◽  
Auditory Perception ◽  
Auditory Brainstem ◽  
Central Auditory Processing ◽  
Visible Part ◽  
Auditory Object ◽  
Mother’S Voice ◽  
The Brain

When one talks about hearing, some may first imagine the auricle (or external ear), which is the only visible part of the auditory system in humans and other mammals. Its shape and size vary among people, but it does not tell us much about a person’s abilities to hear (except perhaps their ability to localize sounds in space, where the shape of the auricle plays a certain role). Most of what is used for hearing is inside the head, particularly in the brain. The inner ear transforms mechanical vibrations into electrical signals; then the auditory nerve sends these signals into the brainstem, where intricate preprocessing occurs. Although auditory brainstem mechanisms are an important part of central auditory processing, it is the processing taking place in the cerebral cortex (with the thalamus as the mediator), which enables auditory perception and cognition. Human speech and the appreciation of music can hardly be imagined without a complex cortical network of specialized regions, each contributing different aspects of auditory cognitive abilities. During the evolution of these abilities in higher vertebrates, especially birds and mammals, the cortex played a crucial role, so a great deal of what is referred to as central auditory processing happens there. Whether it is the recognition of one’s mother’s voice, listening to Pavarotti singing or Yo-Yo Ma playing the cello, hearing or reading Shakespeare’s sonnets, it will evoke electrical vibrations in the auditory cortex, but it does not end there. Large parts of frontal and parietal cortex receive auditory signals originating in auditory cortex, forming processing streams for auditory object recognition and auditory-motor control, before being channeled into other parts of the brain for comprehension and enjoyment.

scholarly journals Migraine with Prolonged Aura: Correlation of Clinical and EEG Features

1995 ◽  
Vol 8 (2) ◽  
pp. 109-114 ◽  
Author(s):  
A. O. Ogunyemi
Keyword(s):  
Cerebral Cortex ◽  
Migraine With Aura ◽  
Slow Waves ◽  
Spreading Cortical Depression ◽  
Eeg Abnormalities ◽  
Cortical Depression ◽  
Eeg Changes ◽  
Eeg Recordings ◽  
The Brain

Migraine with prolonged aura has rarely been examined with regard to the sequence of the neurological symptoms and the associated EEG changes. This report describes five patients who underwent clinical assessment and EEG recordings during attacks of migraine with prolonged aura. CT scan of the brain was obtained in four of them. Follow-up EEG was also obtained. The aura symptoms either preceded the headache or were coincident with it. The aura symptoms evolved in a manner consistent with posterior-to-anterior dysfunction of the cerebral cortex. The EEG abnormalities were non-epileptiform and consisted of focal delta slow waves or theta slow waves. The EEG abnormalities showed good correlation with the patients' aura symptoms and resolved when the patients became symptom free. The posterior-to-anterior sequence of the aura symptoms is in accord with the findings during cerebral blood flow studies in patients having migraine with aura. Also the symptoms and EEG changes in our patients indicate dysfunction of the cerebral cortex, consistent with the notion that spreading cortical depression may be the underlying pathophysiological event in migraine with aura.

scholarly journals Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Huiping Jiang ◽  
Demeng Wu ◽  
Rui Jiao ◽  
Zongnan Wang
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Eeg Signal ◽  
Classification Models ◽  
Emotion Classification ◽  
Common Space ◽  
Average Classification Accuracy ◽  
The Common ◽  
The Brain

Electroencephalography (EEG) is the measurement of neuronal activity in different areas of the brain through the use of electrodes. As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network (CNN). However, these methods are still not ideal, and shortcomings have been found in the results of some models of EEG feature extraction and classification. In this study, two CNN models were selected for the extraction and classification of preprocessed data, namely, common spatial patterns- (CSP-) CNN and wavelet transform- (WT-) CNN. Using the CSP-CNN, we first used the common space model to reduce dimensionality and then applied the CNN directly to extract and classify the features of the EEG; while, with the WT-CNN model, we used the wavelet transform to extract EEG features, thereafter applying the CNN for classification. The EEG classification results of these two classification models were subsequently analyzed and compared, with the average classification accuracy of the CSP-CNN model found to be 80.56%, and the average classification accuracy of the WT-CNN model measured to 86.90%. Thus, the findings of this study show that the average classification accuracy of the WT-CNN model was 6.34% higher than that of the CSP-CNN.

scholarly journals Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink Sheikh Md. Rabiul Islam1, Md. Shakibul Islam2

2021 ◽  
pp. 1-7
Author(s):  
Sheikh Md. Rabiul Islam ◽  
◽  
Md. Shakibul Islam ◽  
Keyword(s):  
Signal Generation ◽  
Neural Mass Model ◽  
Pathophysiological Mechanism ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Mass Model ◽  
Detailed Model ◽  
Simulation Based ◽  
Neural Mass ◽  
The Brain

The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.

scholarly journals Scale-Free Coupled Dynamics in Brain Networks Captured by Bivariate Focus-Based Multifractal Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Orestis Stylianou ◽  
Frigyes Samuel Racz ◽  
Andras Eke ◽  
Peter Mukli
Keyword(s):  
Resting State ◽  
Functional Coupling ◽  
Dependent Manner ◽  
Coupled Dynamics ◽  
Scale Free ◽  
Neural Processes ◽  
Eeg Recordings ◽  
Cortical Regions ◽  
The Brain

While most connectivity studies investigate functional connectivity (FC) in a scale-dependent manner, coupled neural processes may also exhibit broadband dynamics, manifesting as power-law scaling of their measures of interdependence. Here we introduce the bivariate focus-based multifractal (BFMF) analysis as a robust tool for capturing such scale-free relations and use resting-state electroencephalography (EEG) recordings of 12 subjects to demonstrate its performance in reconstructing physiological networks. BFMF was employed to characterize broadband FC between 62 cortical regions in a pairwise manner, with all investigated connections being tested for true bivariate multifractality. EEG channels were also grouped to represent the activity of six resting-state networks (RSNs) in the brain, thus allowing for the analysis of within- and between- RSNs connectivity, separately. Most connections featured true bivariate multifractality, which could be attributed to the genuine scale-free coupling of neural dynamics. Bivariate multifractality showed a characteristic topology over the cortex that was highly concordant among subjects. Long-term autocorrelation was higher in within-RSNs, while the degree of multifractality was generally found stronger in between-RSNs connections. These results offer statistical evidence of the bivariate multifractal nature of functional coupling in the brain and validate BFMF as a robust method to capture such scale-independent coupled dynamics.

Sign in / Sign up

Export Citation Format

Share Document