scholarly journals Detection of Burst Suppression Patterns in EEG Using Recurrence Rate

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhenhu Liang ◽  
Yinghua Wang ◽  
Yongshao Ren ◽  
Duan Li ◽  
Logan Voss ◽  
...  
Keyword(s):  
Recurrence Rate ◽  
Brain Activity ◽  
Approximate Entropy ◽  
Energy Operator ◽  
Burst Suppression ◽  
Multiple Comparison ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Nonlinear Energy ◽  
Selection Of

Burst suppression is a unique electroencephalogram (EEG) pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia. It is important to detect burst suppression reliably during the administration of anesthetic or sedative agents, especially for cerebral-protective treatments in various neurosurgical diseases. This study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG. The RP analysis is applied to EEG data containing BSPs collected from 14 patients. Firstly we obtain the best selection of parameters for RP analysis. Then, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated. Then RR was selected as the best BSP index one-way analysis of variance (ANOVA) and multiple comparison tests. Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO). ANOVA and multiple comparison tests showed that the RR could detect BSP and that it was superior to other measures with the highest sensitivity of suppression detection (96.49%, P=0.03). Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems.

scholarly journals An Unsupervised Method for Artefact Removal in EEG Signals

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2302 ◽  
Author(s):  
Angel Mur ◽  
Raquel Dormido ◽  
Natividad Duro
Keyword(s):  
Brain Activity ◽  
Statistical Criterion ◽  
Human Intervention ◽  
Eeg Data ◽  
Artefact Detection ◽  
Artefact Removal ◽  
Supervised Selection ◽  
Electroencephalogram Eeg ◽  
Selection Of ◽  
Original Information

Objective: The activity of the brain can be recorded by means of an electroencephalogram (EEG). An EEG is a multichannel signal related to brain activity. However, EEG presents a wide variety of undesired artefacts. Removal of these artefacts is often done using blind source separation methods (BSS) and mainly those based on Independent Component Analysis (ICA). ICA-based methods are well-accepted in the literature for filtering artefacts and have proved to be satisfactory in most scenarios of interest. Our goal is to develop a generic and unsupervised ICA-based algorithm for EEG artefacts removal. Approach: The proposed algorithm makes use of a new unsupervised artefact detection, ICA and a statistical criterion to automatically select the artefact related independent components (ICs) requiring no human intervention. The algorithm is evaluated using both simulated and real EEG data with artefacts (SEEG and AEEG). A comparison between the proposed unsupervised selection of ICs related to the artefact and other supervised selection is also presented. Main results: A new unsupervised ICA-based algorithm to filter artefacts, where ICs related to each artefact are automatically selected. It can be used in online applications, it preserves most of the original information among the artefacts and removes different types of artefacts. Significance: ICA-based methods for filtering artefacts prevail in the literature. The work in this article is important insofar as it addresses the problem of automatic selection of ICs in ICA-based methods. The selection is unsupervised, avoiding the manual ICs selection or a learning process involved in other methods. Our method is a generic algorithm that allows removing EEG artefacts of various types and, unlike some ICA-based algorithms, it retains most of the original information among the artefacts. Within the algorithm, the artefact detection method implemented does not require human intervention either.

scholarly journals Fractal Methods and Power Spectral Density as Means to Explore EEG Patterns in Patients Undertaking Mental Tasks

2021 ◽  
Vol 5 (4) ◽  
pp. 225
Author(s):  
Carlos Alberto Valentim ◽  
Claudio Marcio Cassela Inacio ◽  
Sergio Adriani David
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Brain Activity ◽  
Time Domain Analysis ◽  
Relevant Information ◽  
Fluctuation Analysis ◽  
Power Spectral ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Mental Tasks

Brain electrical activity recorded as electroencephalogram data provides relevant information that can contribute to a better understanding of pathologies and human behaviour. This study explores extant electroencephalogram (EEG) signals in search of patterns that could differentiate subjects undertaking mental tasks and reveals insights on said data. We estimated the power spectral density of the signals and found that the subjects showed stronger gamma brain waves during activity while presenting alpha waves at rest. We also found that subjects who performed better in those tasks seemed to present less power density in high-frequency ranges, which could imply decreased brain activity during tasks. In a time-domain analysis, we used Hall–Wood and Robust–Genton estimators along with the Hurst exponent by means of a detrented fluctuation analysis and found that the first two fractal measures are capable of better differentiating signals between the rest and activity datasets. The statistical results indicated that the brain region corresponding to Fp channels might be more suitable for analysing EEG data from patients conducting arithmetic tasks. In summary, both frequency- and time-based methods employed in the study provided useful insights and should be preferably used together in EEG analysis.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2019 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2020 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

Nonlinear Signal Processing Techniques Applied to EEG Measurements

2011 ◽  
pp. 324-338
Author(s):  
Christos L. Papadelis ◽  
Chrysoula Koutidou-Papadeli ◽  
Panagiotis D. Bamidis ◽  
Nicos Maglaveras
Keyword(s):  
Brain Activity ◽  
Approximate Entropy ◽  
Fluctuation Analysis ◽  
Linear Measurements ◽  
Nonlinear Signal Processing ◽  
Hypoxic Conditions ◽  
Eeg Data ◽  
Nitrogen Mixture ◽  
Nonlinear Signal ◽  
Processing Techniques

The electrical activity of the brain is sensitive to its oxygen supply, and electroencephalography (EEG) has been proposed as a suitable measurement to detect brain activity alterations induced by hypoxia. Since, linear processing techniques that have been used so far in hypoxia studies are based on false linearity assumptions about the generation of the EEG signal, there is a definite need for nonlinear approaches to be applied on EEG data derived from hypoxic conditions. The aim of the present study is to compare nonlinear techniques’ effectiveness to identify significant variations in EEG due to hypoxia. EEG data from two channels were derived from ten healthy subjects participated in the present study. Oxygen and nitrogen mixture was used to simulate hypoxic conditions that correspond to an altitude of 25.000 feet. Non-linear measurements such as correlation dimension, approximate entropy, Lyapunov exponent and detrended fluctuation analysis (DFA) parameters were estimated for EEG signals. The results of the present study confirm the effectiveness of nonlinear techniques to identify significant variations in EEG, which reflect alterations in cerebral function induced by cerebral hypoxic conditions.

Realtime Recognition of Attention State by Complexity Analysis of Electroencephalogram

2013 ◽  
Vol 385-386 ◽  
pp. 1457-1460
Author(s):  
Li Yu Huang ◽  
Hong Juan Ma ◽  
Lang Jin ◽  
Rong Lu
Keyword(s):  
Support Vector Machine ◽  
Complexity Analysis ◽  
Wavelet Packet ◽  
Approximate Entropy ◽  
Support Vector ◽  
Eeg Rhythms ◽  
Average Performance ◽  
Novel Approach ◽  
Eeg Data ◽  
Electroencephalogram Eeg

The object of this paper is presenting a novel approach to classify the attention state and non-attention state. Firstly, the raw recorded electroencephalogram (EEG) data were decomposed by the algorithm of wavelet packet, several main EEG rhythms were extracted; then a complexity measure of these rhythm signal, approximate entropy (ApEn) was calculated respectively, and the values were used as input vector of a trained support vector machine (SVM), the output of this SVM will be the result of classification. The average performance obtained for the proposed scheme in classification is: sensitivity 73.7%, specificity 71.4% and accuracy 72.5%.

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.

scholarly journals Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research

2021 ◽  
Author(s):  
Jaclyn L. Farrens ◽  
Aaron M. Simmons ◽  
Steven J. Luck ◽  
Emily S. Kappenman
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Neuroscience Research ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Brain Processing ◽  
Resolution Measure ◽  
Electroencephalogram Eeg

Abstract Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

scholarly journals Construction and Analysis of Emotion Computing Model Based on LSTM

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huiping Jiang ◽  
Rui Jiao ◽  
Zequn Wang ◽  
Ting Zhang ◽  
Licheng Wu
Keyword(s):  
Short Term Memory ◽  
Brain Activity ◽  
Extraction Methods ◽  
Signal Timing ◽  
Electrical Brain Activity ◽  
Average Accuracy ◽  
Eeg Data ◽  
Emotion Generation ◽  
Lstm Network ◽  
Electroencephalogram Eeg

The electroencephalogram (EEG) is the most common method used to study emotions and capture electrical brain activity changes. Long short-term memory (LSTM) processes the temporal characteristics of data and is mostly used for emotional text and speech recognition. Since an EEG involves a time series signal, this article mainly studied the introduction of LSTM for emotional EEG recognition. First, an ALL-LSTM model with a four-layered LSTM network was established in which the average accuracy rate for emotional classification reached 86.48%. Second, four EEG characteristics were extracted via the wavelet transform (WT) using the LSTM-based sentiment classification network. The experimental results showed that the best average classification accuracy of these four features was 73.48%. This was 13% lower than in the ALL-LSTM model, indicating that inappropriate feature extraction methods could destroy the timing of EEG signals. LSTM can be used to thoroughly examine EEG signal timing and preprocessed EEG data. The accuracy and stability of the ALL-LSTM model are significantly superior to those of the WT-LSTM model. The result showed that the process of emotion generation based on EEG is sequential. Compared with EEG emotion extraction using WT, the raw EEG signal’s timing is more suitable for the LSTM network.

scholarly journals EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5092
Author(s):  
Tran-Dac-Thinh Phan ◽  
Soo-Hyung Kim ◽  
Hyung-Jeong Yang ◽  
Guee-Sang Lee
Keyword(s):  
Emotion Recognition ◽  
Binary Classification ◽  
Eeg Signals ◽  
Frequency Bands ◽  
Multi Scale ◽  
Eeg Data ◽  
Crucial Information ◽  
The Time Domain ◽  
Electroencephalogram Eeg ◽  
Selection Of

Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data have been drawing attention thanks to their capability in countering the effect of deceptive external expressions of humans, like faces or speeches. Emotion recognition based on EEG signals heavily relies on the features and their delineation, which requires the selection of feature categories converted from the raw signals and types of expressions that could display the intrinsic properties of an individual signal or a group of them. Moreover, the correlation or interaction among channels and frequency bands also contain crucial information for emotional state prediction, and it is commonly disregarded in conventional approaches. Therefore, in our method, the correlation between 32 channels and frequency bands were put into use to enhance the emotion prediction performance. The extracted features chosen from the time domain were arranged into feature-homogeneous matrices, with their positions following the corresponding electrodes placed on the scalp. Based on this 3D representation of EEG signals, the model must have the ability to learn the local and global patterns that describe the short and long-range relations of EEG channels, along with the embedded features. To deal with this problem, we proposed the 2D CNN with different kernel-size of convolutional layers assembled into a convolution block, combining features that were distributed in small and large regions. Ten-fold cross validation was conducted on the DEAP dataset to prove the effectiveness of our approach. We achieved the average accuracies of 98.27% and 98.36% for arousal and valence binary classification, respectively.

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