Ordinal Pattern Based Complexity Analysis for EEG Activity Evoked by Manual Acupuncture in Healthy Subjects

2014 ◽  
Vol 24 (02) ◽  
pp. 1450018 ◽  
Author(s):  
Guosheng Yi ◽  
Jiang Wang ◽  
Kai-Ming Tsang ◽  
Wai-Lok Chan ◽  
Xile Wei ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Pain Treatment ◽  
Complexity Analysis ◽  
Brain Activity ◽  
Permutation Entropy ◽  
Manual Acupuncture ◽  
Nonlinear Complexity ◽  
Eeg Data ◽  
Neurophysiological Mechanisms ◽  
Ordinal Patterns

Manual acupuncture (MA) is widely used in Traditional Chinese Medicine clinic for pain treatment and controlling stress. To investigate how MA modulates brain activities, electroencephalograph (EEG) signals are recorded with 20 channels by MA at ST36 of right leg in 11 healthy subjects during rest. Two novel nonlinear measures based on ordinal patterns of EEG series, i.e. permutation entropy (PE) and order index (OI), are adopted to investigate the nonlinear complexity characteristic in EEG data at different acupuncture states. It is observed that the recorded EEG series during and after MA have higher PE values and lower OI values compared to before MA. The results show that MA at ST36 can increase EEG complexity, which is especially obvious during MA. Our findings suggest that the PE and OI measures are promising methods to reveal EEG dynamical changes associated with MA stimulus, which could provide a potential for further exploring the interactions between acupuncture and brain activity. Moreover, these preliminary conclusions highlight the beneficial modulations of brain activity by MA, which could contribute to understanding the acupuncture effects on brain, as well as the neurophysiological mechanisms underlying MA.

ACCURATE DETECTION OF SEIZURE USING NONLINEAR PARAMETERS EXTRACTED FROM EEG SIGNALS

2019 ◽  
Vol 19 (01) ◽  
pp. 1940004 ◽  
Author(s):  
JAHMUNAH VICNESH ◽  
YUKI HAGIWARA
Keyword(s):  
Kolmogorov Complexity ◽  
Brain Activity ◽  
Mean Value ◽  
Permutation Entropy ◽  
Cad System ◽  
Eeg Data ◽  
Nonlinear Features ◽  
Best Parameter ◽  
Value Decomposition

Electroencephalography (EEG) is the graphical recording of electrical activity along the scalp. The EEG signal monitors brain activity noninvasively with a high accuracy of milliseconds and provides valuable discernment about the brain’s state. It is also sensitive in detecting spikes in epilepsy. Computer-aided diagnosis (CAD) tools allow epilepsy to be diagnosed by evading invasive methods. This paper presents a novel CAD system for epilepsy using other linear features together with Hjorth’s nonlinear features such as mobility, complexity, activity and Kolmogorov complexity. The proposed method uses MATLAB software to extract the nonlinear features from the EEG data. The optimal features are selected using the statistical analysis, ANOVA (analysis of variance) test for classification. Once selected, they are fed into the decision tree (DT) for the classification of the different epileptic classes. The proposed method affirms that four nonlinear features, Kolmogorov complexity, singular value decomposition, mobility and permutation entropy are sufficient to provide the highest accuracy of 93%, sensitivity of 97%, specificity of 88% and positive predictive value (PPV) of 94%, with the DT classifier. The mean value is the highest in the ictal stage for the Kolmogorov complexity proving it to have the best variation. It also has the highest [Formula: see text]-value of 300.439 portraying it to be the best parameter that is favourable for the clinical diagnosis of epilepsy, when used together with the DT classifier, for a duration of 23.6[Formula: see text]s of EEG data.

scholarly journals Time-frequency analysis methods and their application in developmental EEG data

2021 ◽  
Author(s):  
Santiago Morales ◽  
Maureen Bowers
Keyword(s):  
Brain Activity ◽  
Event Related Potentials ◽  
Phase Lag ◽  
Phase Synchrony ◽  
Time Frequency ◽  
Use Of Time ◽  
Eeg Data ◽  
Related Potentials ◽  
Neurophysiological Mechanisms ◽  
The Fourier Transform

EEG provides a rich measure of brain activity that can be characterized as neuronal oscillations. However, most developmental EEG work to date has focused on analyzing EEG data as Event-Related Potentials (ERPs) or power based on the Fourier transform. While these measures have been productive, they do not leverage all the information contained within the EEG signal. Namely, ERP analyses ignore non-phase-locked signals and Fourier-based power analyses ignore temporal information. Time-frequency analyses can better characterize the oscillations contained in the EEG data. By separating power and phase information across different frequencies, time-frequency measures provide a closer interpretation of the neurophysiological mechanisms, facilitate translation across neurophysiology disciplines, and capture processes not observed by ERP or Fourier-based analyses (e.g., connectivity). Despite their unique contributions, a literature review of this journal reveals that time-frequency analyses of EEG are yet to be embraced by the developmental cognitive neuroscience field. This manuscript presents a conceptual introduction to time-frequency analyses for developmental researchers. To facilitate the use of time-frequency analyses, we include a tutorial of accessible scripts, based on Cohen (2014), to calculate time-frequency power (signal strength), inter-trial phase synchrony (signal consistency), and two types of phase-based connectivity (inter-channel phase synchrony and weighted phase lag index).

scholarly journals The right thalamus may play an important role in anesthesia-awakening regulation in frogs

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4516 ◽  
Author(s):  
Yanzhu Fan ◽  
Xizi Yue ◽  
Fei Xue ◽  
Steven E. Brauth ◽  
Yezhong Tang ◽  
...  
Keyword(s):  
General Anesthesia ◽  
South African ◽  
Brain Activity ◽  
Approximate Entropy ◽  
Permutation Entropy ◽  
Amphibian Species ◽  
The Awakening ◽  
Eeg Data ◽  
The Right

BackgroundPrevious studies have shown that the mammalian thalamus is a key structure for anesthesia-induced unconsciousness and anesthesia-awakening regulation. However, both the dynamic characteristics and probable lateralization of thalamic functioning during anesthesia-awakening regulation are not fully understood, and little is known of the evolutionary basis of the role of the thalamus in anesthesia-awakening regulation.MethodsAn amphibian species, the South African clawed frog (Xenopus laevis) was used in the present study. The frogs were immersed in triciane methanesulfonate (MS-222) for general anesthesia. Electroencephalogram (EEG) signals were recorded continuously from both sides of the telencephalon, diencephalon (thalamus) and mesencephalon during the pre-anesthesia stage, administration stage, recovery stage and post-anesthesia stage. EEG data was analyzed including calculation of approximate entropy (ApEn) and permutation entropy (PE).ResultsBoth ApEn and PE values differed significantly between anesthesia stages, with the highest values occurring during the awakening period and the lowest values during the anesthesia period. There was a significant correlation between the stage durations and ApEn or PE values during anesthesia-awakening cycle primarily for the right diencephalon (right thalamus). ApEn and PE values for females were significantly higher than those for males.DiscussionApEn and PE measurements are suitable for estimating depth of anesthesia and complexity of amphibian brain activity. The right thalamus appears physiologically positioned to play an important role in anesthesia-awakening regulation in frogs indicating an early evolutionary origin of the role of the thalamus in arousal and consciousness in land vertebrates. Sex differences exist in the neural regulation of general anesthesia in frogs.

Su1967 Gender Differences in Brain Activity During Visceral Placebo Analgesia in Healthy Subjects

2012 ◽  
Vol 142 (5) ◽  
pp. S-548
Author(s):  
I-Ju Lin ◽  
Ching-Liang Lu ◽  
Jen-Chuen Hsieh ◽  
Full-Young Chang
Keyword(s):  
Gender Differences ◽  
Healthy Subjects ◽  
Brain Activity ◽  
Placebo Analgesia

scholarly journals Regularized two-step brain activity reconstruction from spatiotemporal EEG data

2004 ◽  
Author(s):  
Teodor I. Alecu ◽  
Sviatoslav Voloshynovskiy ◽  
Thierry Pun
Keyword(s):  
Brain Activity ◽  
Eeg Data

scholarly journals ConvDip: A convolutional neural network for better M/EEG Source Imaging

2020 ◽  
Author(s):  
Lukas Hecker ◽  
Rebekka Rupprecht ◽  
Ludger Tebartz van Elst ◽  
Juergen Kornmeier
Keyword(s):  
Neural Network ◽  
Inverse Problem ◽  
Convolutional Neural Network ◽  
Brain Activity ◽  
Clinical Diagnostics ◽  
Dipole Model ◽  
Source Imaging ◽  
Eeg Source Imaging ◽  
Eeg Data ◽  
Inverse Solutions

AbstractEEG and MEG are well-established non-invasive methods in neuroscientific research and clinical diagnostics. Both methods provide a high temporal but low spatial resolution of brain activity. In order to gain insight about the spatial dynamics of the M/EEG one has to solve the inverse problem, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipoles sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods (eLORETA and LCMV beamforming) on all focused performance measures. (3) It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. (4) It produces plausible inverse solutions for real-world EEG recordings and needs less than 40 ms for a single forward pass. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG and MEG data, with high relevance for clinical applications, e.g. in epileptology and real time applications.

scholarly journals CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

2020 ◽  
Author(s):  
Diego Fabian Collazos Huertas ◽  
Andres Marino Alvarez Meza ◽  
German Castellanos Dominguez
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Continuous Wavelet Transform ◽  
Brain Activity ◽  
Machine Learning Techniques ◽  
Continuous Wavelet ◽  
Brain Response ◽  
Power Spectral ◽  
Learning Techniques ◽  
Eeg Data

Abstract Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.

scholarly journals BSoniq: A 3-D EEG Sound Installation

2017 ◽  
Author(s):  
Marlene Mathew ◽  
Mert Cetinkaya ◽  
Agnieszka Roginska
Keyword(s):  
Brain Activity ◽  
Spatial Location ◽  
Data Access ◽  
Mental States ◽  
Brain Wave ◽  
Sound Installation ◽  
Eeg Data ◽  
Additional Process ◽  
Event Based ◽  
Exciting Possibility

Brain Computer Interface (BCI) methods have received a lot of attention in the past several decades, owing to the exciting possibility of computer-aided communication with the outside world. Most BCIs allow users to control an external entity such as games, prosthetics, musical output etc. or are used for offline medical diagnosis processing. Most BCIs that provide neurofeedback, usually categorize the brainwaves into mental states for the user to interact with. Raw brainwave interaction by the user is not usually a feature that is readily available for a lot of popular BCIs. If there is, the user has to pay for or go through an additional process for raw brain wave data access and interaction. BSoniq is a multi-channel interactive neurofeedback installation which, allows for real-time sonification and visualization of electroencephalogram (EEG) data. This EEG data provides multivariate information about human brain activity. Here, a multivariate event-based sonification is proposed using 3D spatial location to provide cues about these particular events. With BSoniq, users can listen to the various sounds (raw brain waves) emitted from their brain or parts of their brain and perceive their own brainwave activities in a 3D spatialized surrounding giving them a sense that they are inside their own heads.

scholarly journals The Influence of Filters on EEG-ERP Testing: Analysis of Motor Cortex in Healthy Subjects

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7711
Author(s):  
Ilona Karpiel ◽  
Zofia Kurasz ◽  
Rafał Kurasz ◽  
Klaudia Duch
Keyword(s):  
Gold Standard ◽  
Healthy Subjects ◽  
Data Interpretation ◽  
Eeg Signal ◽  
Sensorimotor Area ◽  
Eeg Data ◽  
Reduction Methods ◽  
Characteristic Values ◽  
No Gold Standard ◽  
Correct Data

The raw EEG signal is always contaminated with many different artifacts, such as muscle movements (electromyographic artifacts), eye blinking (electrooculographic artifacts) or power line disturbances. All artifacts must be removed for correct data interpretation. However, various noise reduction methods significantly influence the final shape of the EEG signal and thus its characteristic values, latency and amplitude. There are several types of filters to eliminate noise early in the processing of EEG data. However, there is no gold standard for their use. This article aims to verify and compare the influence of four various filters (FIR, IIR, FFT, NOTCH) on the latency and amplitude of the EEG signal. By presenting a comparison of selected filters, the authors intend to raise awareness among researchers as regards the effects of known filters on latency and amplitude in a selected area—the sensorimotor area.

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