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Simultaneous EEG-fMRI for working memory of the human brain

Ahmad RF, Malik AS, Kamel N, Reza F, Abdullah JM

Australas Phys Eng Sci Med, 2016 Jun;39(2):363-78.
PMID: 27043850 DOI: 10.1007/s13246-016-0438-x

Memory plays an important role in human life. Memory can be divided into two categories, i.e., long term memory and short term memory (STM). STM or working memory (WM) stores information for a short span of time and it is used for information manipulations and fast response activities. WM is generally involved in the higher cognitive functions of the brain. Different studies have been carried out by researchers to understand the WM process. Most of these studies were based on neuroimaging modalities like fMRI, EEG, MEG etc., which use standalone processes. Each neuroimaging modality has some pros and cons. For example, EEG gives high temporal resolution but poor spatial resolution. On the other hand, the fMRI results have a high spatial resolution but poor temporal resolution. For a more in depth understanding and insight of what is happening inside the human brain during the WM process or during cognitive tasks, high spatial as well as high temporal resolution is desirable. Over the past decade, researchers have been working to combine different modalities to achieve a high spatial and temporal resolution at the same time. Developments of MRI compatible EEG equipment in recent times have enabled researchers to combine EEG-fMRI successfully. The research publications in simultaneous EEG-fMRI have been increasing tremendously. This review is focused on the WM research involving simultaneous EEG-fMRI data acquisition and analysis. We have covered the simultaneous EEG-fMRI application in WM and data processing. Also, it adds to potential fusion methods which can be used for simultaneous EEG-fMRI for WM and cognitive tasks.

Matched MeSH terms: Electroencephalography/methods*
Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA

Javed E, Faye I, Malik AS, Abdullah JM

J Neurosci Methods, 2017 11 01;291:150-165.
PMID: 28842191 DOI: 10.1016/j.jneumeth.2017.08.020

BACKGROUND: Simultaneous electroencephalography (EEG) and functional magnetic resonance image (fMRI) acquisitions provide better insight into brain dynamics. Some artefacts due to simultaneous acquisition pose a threat to the quality of the data. One such problematic artefact is the ballistocardiogram (BCG) artefact.
METHODS: We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.
RESULTS: The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.
COMPARISON WITH EXISTING METHODS: Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.
CONCLUSIONS: The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.

Matched MeSH terms: Electroencephalography/methods*
An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank

Sharma M, Goyal D, Achuth PV, Acharya UR

Comput Biol Med, 2018 07 01;98:58-75.
PMID: 29775912 DOI: 10.1016/j.compbiomed.2018.04.025

Sleep related disorder causes diminished quality of lives in human beings. Sleep scoring or sleep staging is the process of classifying various sleep stages which helps to detect the quality of sleep. The identification of sleep-stages using electroencephalogram (EEG) signals is an arduous task. Just by looking at an EEG signal, one cannot determine the sleep stages precisely. Sleep specialists may make errors in identifying sleep stages by visual inspection. To mitigate the erroneous identification and to reduce the burden on doctors, a computer-aided EEG based system can be deployed in the hospitals, which can help identify the sleep stages, correctly. Several automated systems based on the analysis of polysomnographic (PSG) signals have been proposed. A few sleep stage scoring systems using EEG signals have also been proposed. But, still there is a need for a robust and accurate portable system developed using huge dataset. In this study, we have developed a new single-channel EEG based sleep-stages identification system using a novel set of wavelet-based features extracted from a large EEG dataset. We employed a novel three-band time-frequency localized (TBTFL) wavelet filter bank (FB). The EEG signals are decomposed using three-level wavelet decomposition, yielding seven sub-bands (SBs). This is followed by the computation of discriminating features namely, log-energy (LE), signal-fractal-dimensions (SFD), and signal-sample-entropy (SSE) from all seven SBs. The extracted features are ranked and fed to the support vector machine (SVM) and other supervised learning classifiers. In this study, we have considered five different classification problems (CPs), (two-class (CP-1), three-class (CP-2), four-class (CP-3), five-class (CP-4) and six-class (CP-5)). The proposed system yielded accuracies of 98.3%, 93.9%, 92.1%, 91.7%, and 91.5% for CP-1 to CP-5, respectively, using 10-fold cross validation (CV) technique.

Matched MeSH terms: Electroencephalography/methods*
Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals

Ay B, Yildirim O, Talo M, Baloglu UB, Aydin G, Puthankattil SD, et al.

J Med Syst, 2019 May 28;43(7):205.
PMID: 31139932 DOI: 10.1007/s10916-019-1345-y

Depression affects large number of people across the world today and it is considered as the global problem. It is a mood disorder which can be detected using electroencephalogram (EEG) signals. The manual detection of depression by analyzing the EEG signals requires lot of experience, tedious and time consuming. Hence, a fully automated depression diagnosis system developed using EEG signals will help the clinicians. Therefore, we propose a deep hybrid model developed using convolutional neural network (CNN) and long-short term memory (LSTM) architectures to detect depression using EEG signals. In the deep model, temporal properties of the signals are learned with CNN layers and the sequence learning process is provided through the LSTM layers. In this work, we have used EEG signals obtained from left and right hemispheres of the brain. Our work has provided 99.12% and 97.66% classification accuracies for the right and left hemisphere EEG signals respectively. Hence, we can conclude that the developed CNN-LSTM model is accurate and fast in detecting the depression using EEG signals. It can be employed in psychiatry wards of the hospitals to detect the depression using EEG signals accurately and thus aid the psychiatrists.

Matched MeSH terms: Electroencephalography/methods*
Fulltext An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals

Khare SK, Acharya UR

Comput Biol Med, 2023 Mar;155:106676.
PMID: 36827785 DOI: 10.1016/j.compbiomed.2023.106676

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects a person's sleep, mood, anxiety, and learning. Early diagnosis and timely medication can help individuals with ADHD perform daily tasks without difficulty. Electroencephalogram (EEG) signals can help neurologists to detect ADHD by examining the changes occurring in it. The EEG signals are complex, non-linear, and non-stationary. It is difficult to find the subtle differences between ADHD and healthy control EEG signals visually. Also, making decisions from existing machine learning (ML) models do not guarantee similar performance (unreliable).
METHOD: The paper explores a combination of variational mode decomposition (VMD), and Hilbert transform (HT) called VMD-HT to extract hidden information from EEG signals. Forty-one statistical parameters extracted from the absolute value of analytical mode functions (AMF) have been classified using the explainable boosted machine (EBM) model. The interpretability of the model is tested using statistical analysis and performance measurement. The importance of the features, channels and brain regions has been identified using the glass-box and black-box approach. The model's local and global explainability has been visualized using Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Morris sensitivity. To the best of our knowledge, this is the first work that explores the explainability of the model prediction in ADHD detection, particularly for children.
RESULTS: Our results show that the explainable model has provided an accuracy of 99.81%, a sensitivity of 99.78%, 99.84% specificity, an F-1 measure of 99.83%, the precision of 99.87%, a false detection rate of 0.13%, and Mathew's correlation coefficient, negative predicted value, and critical success index of 99.61%, 99.73%, and 99.66%, respectively in detecting the ADHD automatically with ten-fold cross-validation. The model has provided an area under the curve of 100% while the detection rate of 99.87% and 99.73% has been obtained for ADHD and HC, respectively.
CONCLUSIONS: The model show that the interpretability and explainability of frontal region is highest compared to pre-frontal, central, parietal, occipital, and temporal regions. Our findings has provided important insight into the developed model which is highly reliable, robust, interpretable, and explainable for the clinicians to detect ADHD in children. Early and rapid ADHD diagnosis using robust explainable technologies may reduce the cost of treatment and lessen the number of patients undergoing lengthy diagnosis procedures.

Matched MeSH terms: Electroencephalography/methods
SchizoNET: a robust and accurate Margenau-Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals

Khare SK, Bajaj V, Acharya UR

Physiol Meas, 2023 Mar 08;44(3).
PMID: 36787641 DOI: 10.1088/1361-6579/acbc06

Objective.Schizophrenia (SZ) is a severe chronic illness characterized by delusions, cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking. Timely detection and treatment of SZ are necessary to avoid long-term consequences. Electroencephalogram (EEG) signals are one form of a biomarker that can reveal hidden changes in the brain during SZ. However, the EEG signals are non-stationary in nature with low amplitude. Therefore, extracting the hidden information from the EEG signals is challenging.Approach.The time-frequency domain is crucial for the automatic detection of SZ. Therefore, this paper presents the SchizoNET model combining the Margenau-Hill time-frequency distribution (MH-TFD) and convolutional neural network (CNN). The instantaneous information of EEG signals is captured in the time-frequency domain using MH-TFD. The time-frequency amplitude is converted to two-dimensional plots and fed to the developed CNN model.Results.The SchizoNET model is developed using three different validation techniques, including holdout, five-fold cross-validation, and ten-fold cross-validation techniques using three separate public SZ datasets (Dataset 1, 2, and 3). The proposed model achieved an accuracy of 97.4%, 99.74%, and 96.35% on Dataset 1 (adolescents: 45 SZ and 39 HC subjects), Dataset 2 (adults: 14 SZ and 14 HC subjects), and Dataset 3 (adults: 49 SZ and 32 HC subjects), respectively. We have also evaluated six performance parameters and the area under the curve to evaluate the performance of our developed model.Significance.The SchizoNET is robust, effective, and accurate, as it performed better than the state-of-the-art techniques. To the best of our knowledge, this is the first work to explore three publicly available EEG datasets for the automated detection of SZ. Our SchizoNET model can help neurologists detect the SZ in various scenarios.

Matched MeSH terms: Electroencephalography/methods
Complexity-based classification of EEG signal in normal subjects and patients with epilepsy

Namazi H, Aghasian E, Ala TS

Technol Health Care, 2020;28(1):57-66.
PMID: 31104032 DOI: 10.3233/THC-181579

Analysis of human brain activity is an important topic in human neuroscience. Human brain activity can be studied by analyzing the electroencephalography (EEG) signal. In this way, scientists have employed several techniques that investigate nonlinear dynamics of EEG signals. Fractal theory as a promising technique has shown its capabilities to analyze the nonlinear dynamics of time series. Since EEG signals have fractal patterns, in this research we analyze the variations of fractal dynamics of EEG signals between four datasets that were collected from healthy subjects with open-eyes and close-eyes conditions, patients with epilepsy who did and patients who did not face seizures. The obtained results showed that EEG signal during seizure has greatest complexity and the EEG signal during the seizure-free interval has lowest complexity. In order to verify the obtained results in case of fractal analysis, we employ approximate entropy, which indicates the randomness of time series. The obtained results in case of approximate entropy certified the fractal analysis results. The obtained results in this research show the effectiveness of fractal theory to investigate the nonlinear structure of EEG signal between different conditions.

Matched MeSH terms: Electroencephalography/methods*
Fractal-based classification of electroencephalography (EEG) signals in healthy adolescents and adolescents with symptoms of schizophrenia

Namazi H, Aghasian E, Ala TS

Technol Health Care, 2019;27(3):233-241.
PMID: 30829625 DOI: 10.3233/THC-181497

Brain activity analysis is an important research area in the field of human neuroscience. Moreover, a subcategory in this field is the classification of brain activity in terms of different brain disorders. Since the Electroencephalography (EEG) signal is, in fact, a non-linear time series, employing techniques to investigate its non-linear structure is rather crucial. In this study, we evaluate the non-linear structure of the EEG signal between healthy and schizophrenic adolescents using fractal theory. The results of our analysis revealed that in terms of all recording channels, the EEG signal of healthy subjects is more complex compared to the ones suffering from schizophrenia. The statistical analysis also indicated that there is a significant difference in the complex structure of the EEG signal between these two groups of subjects. We also utilized approximate entropy in our analysis in order to verify the obtained results of the fractal analysis. The result of the entropy analysis suggested that EEG signal for healthy subjects is less random compared to the EEG signal in schizophrenic individuals. In addition, the employed methodology in this research can be further investigated in order to classify the brain activity in terms of other brain disorders, where one can explore how the complex structure of the EEG signal alters between them.

Matched MeSH terms: Electroencephalography/methods*
Fulltext Evolution of electroencephalogram signal analysis techniques during anesthesia

Al-Kadi MI, Reaz MB, Ali MA

Sensors (Basel), 2013;13(5):6605-35.
PMID: 23686141 DOI: 10.3390/s130506605

Biosignal analysis is one of the most important topics that researchers have tried to develop during the last century to understand numerous human diseases. Electroencephalograms (EEGs) are one of the techniques which provides an electrical representation of biosignals that reflect changes in the activity of the human brain. Monitoring the levels of anesthesia is a very important subject, which has been proposed to avoid both patient awareness caused by inadequate dosage of anesthetic drugs and excessive use of anesthesia during surgery. This article reviews the bases of these techniques and their development within the last decades and provides a synopsis of the relevant methodologies and algorithms that are used to analyze EEG signals. In addition, it aims to present some of the physiological background of the EEG signal, developments in EEG signal processing, and the effective methods used to remove various types of noise. This review will hopefully increase efforts to develop methods that use EEG signals for determining and classifying the depth of anesthesia with a high data rate to produce a flexible and reliable detection device.

Matched MeSH terms: Electroencephalography/methods*
Fulltext Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification

Lai CQ, Ibrahim H, Abdullah MZ, Abdullah JM, Suandi SA, Azman A

Comput Intell Neurosci, 2019;2019:7895924.
PMID: 31281339 DOI: 10.1155/2019/7895924

Biometric is an important field that enables identification of an individual to access their sensitive information and asset. In recent years, electroencephalography- (EEG-) based biometrics have been popularly explored by researchers because EEG is able to distinct between two individuals. The literature reviews have shown that convolutional neural network (CNN) is one of the classification approaches that can avoid the complex stages of preprocessing, feature extraction, and feature selection. Therefore, CNN is suggested to be one of the efficient classifiers for biometric identification. Conventionally, input to CNN can be in image or matrix form. The objective of this paper is to explore the arrangement of EEG for CNN input to investigate the most suitable input arrangement of EEG towards the performance of EEG-based identification. EEG datasets that are used in this paper are resting state eyes open (REO) and resting state eyes close (REC) EEG. Six types of data arrangement are compared in this paper. They are matrix of amplitude versus time, matrix of energy versus time, matrix of amplitude versus time for rearranged channels, image of amplitude versus time, image of energy versus time, and image of amplitude versus time for rearranged channels. It was found that the matrix of amplitude versus time for each rearranged channels using the combination of REC and REO performed the best for biometric identification, achieving validation accuracy and test accuracy of 83.21% and 79.08%, respectively.

Matched MeSH terms: Electroencephalography/methods
Spectral estimation of nonstationary EEG using particle filtering with application to event-related desynchronization (ERD)

Ting CM, Salleh ShH, Zainuddin ZM, Bahar A

IEEE Trans Biomed Eng, 2011 Feb;58(2):321-31.
PMID: 21257361 DOI: 10.1109/TBME.2010.2088396

This paper proposes non-Gaussian models for parametric spectral estimation with application to event-related desynchronization (ERD) estimation of nonstationary EEG. Existing approaches for time-varying spectral estimation use time-varying autoregressive (TVAR) state-space models with Gaussian state noise. The parameter estimation is solved by a conventional Kalman filtering. This study uses non-Gaussian state noise to model autoregressive (AR) parameter variation with estimation by a Monte Carlo particle filter (PF). Use of non-Gaussian noise such as heavy-tailed distribution is motivated by its ability to track abrupt and smooth AR parameter changes, which are inadequately modeled by Gaussian models. Thus, more accurate spectral estimates and better ERD tracking can be obtained. This study further proposes a non-Gaussian state space formulation of time-varying autoregressive moving average (TVARMA) models to improve the spectral estimation. Simulation on TVAR process with abrupt parameter variation shows superior tracking performance of non-Gaussian models. Evaluation on motor-imagery EEG data shows that the non-Gaussian models provide more accurate detection of abrupt changes in alpha rhythm ERD. Among the proposed non-Gaussian models, TVARMA shows better spectral representations while maintaining reasonable good ERD tracking performance.

Matched MeSH terms: Electroencephalography/methods*
Assessment of computerized analysis algorithms for one dimension physiologic data with multiple databases

Lee YK, Bister M, Salleh YM, Blanchfield P

Annu Int Conf IEEE Eng Med Biol Soc, 2008;2008:5004-7.
PMID: 19163841 DOI: 10.1109/IEMBS.2008.4650338

Software technology enables computerized analysis to offer second opinion in various screening and diagnostic tasks to assist the clinicians. Yet, the performance of these computerized methods for medical images is questioned by experts in CAD research, owing to the use of different databases and criteria for evaluating the computer results for comparison. This paper intends to substantiate this statement by illustrating the effects of such issues with the use of 1D physiologic data and multiple databases. For this purpose, the detection of desaturation events in Sp02 and spike events in EEG are used. This is the first time that comparison between different algorithms on a common basis is carried out on an individual effort. The appraisal for all the algorithms is made on the same databases and criteria. It is surprising to find that issues for 2/3D images concur with those found in 1D data here. In evaluating the accuracy of a new algorithm, a single independent database gives results fast. This paper reveals weaknesses of such an approach. It is hoped that the supportive evidence shown here is enough for researchers to innovate a better platform for credibility in reporting performance comparison of computerized analysis algorithms.

Matched MeSH terms: Electroencephalography/methods*
Fulltext The usefulness of early ultrasonography, electroencephalography and clinical parameters in predicting adverse outcomes in asphyxiated term infants

Ong LC, Kanaheswari Y, Chandran V, Rohana J, Yong SC, Boo NY

Singapore Med J, 2009 Jul;50(7):705-9.
PMID: 19644627

The early identification of asphyxiated infants at high risk of adverse outcomes and the early selection of those who might benefit from neuroprotective therapies are required. A prospective observational study was conducted to determine if there were any early clinical, neuroimaging or neurophysiological parameters that might predict the outcome in term newborns with asphyxia.

Matched MeSH terms: Electroencephalography/methods*
Fulltext EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review

Al-Quraishi MS, Elamvazuthi I, Daud SA, Parasuraman S, Borboni A

Sensors (Basel), 2018 Oct 07;18(10).
PMID: 30301238 DOI: 10.3390/s18103342

Electroencephalography (EEG) signals have great impact on the development of assistive rehabilitation devices. These signals are used as a popular tool to investigate the functions and the behavior of the human motion in recent research. The study of EEG-based control of assistive devices is still in early stages. Although the EEG-based control of assistive devices has attracted a considerable level of attention over the last few years, few studies have been carried out to systematically review these studies, as a means of offering researchers and experts a comprehensive summary of the present, state-of-the-art EEG-based control techniques used for assistive technology. Therefore, this research has three main goals. The first aim is to systematically gather, summarize, evaluate and synthesize information regarding the accuracy and the value of previous research published in the literature between 2011 and 2018. The second goal is to extensively report on the holistic, experimental outcomes of this domain in relation to current research. It is systematically performed to provide a wealthy image and grounded evidence of the current state of research covering EEG-based control for assistive rehabilitation devices to all the experts and scientists. The third goal is to recognize the gap of knowledge that demands further investigation and to recommend directions for future research in this area.

Matched MeSH terms: Electroencephalography/methods*
A deep learning based model using RNN-LSTM for the Detection of Schizophrenia from EEG data

Supakar R, Satvaya P, Chakrabarti P

Comput Biol Med, 2022 Dec;151(Pt A):106225.
PMID: 36306576 DOI: 10.1016/j.compbiomed.2022.106225

Normal life can be ensured for schizophrenic patients if diagnosed early. Electroencephalogram (EEG) carries information about the brain network connectivity which can be used to detect brain anomalies that are indicative of schizophrenia. Since deep learning is capable of automatically extracting the significant features and make classifications, the authors proposed a deep learning based model using RNN-LSTM to analyze the EEG signal data to diagnose schizophrenia. The proposed model used three dense layers on top of a 100 dimensional LSTM. EEG signal data of 45 schizophrenic patients and 39 healthy subjects were used in the study. Dimensionality reduction algorithm was used to obtain an optimal feature set and the classifier was run with both sets of data. An accuracy of 98% and 93.67% were obtained with the complete feature set and the reduced feature set respectively. The robustness of the model was evaluated using model performance measure and combined performance measure. Outcomes were compared with the outcome obtained with traditional machine learning classifiers such as Random Forest, SVM, FURIA, and AdaBoost, and the proposed model was found to perform better with the complete dataset. When compared with the result of the researchers who worked with the same set of data using either CNN or RNN, the proposed model's accuracy was either better or comparable to theirs.

Matched MeSH terms: Electroencephalography/methods
Fulltext Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring

Shivaraja TR, Remli R, Kamal N, Wan Zaidi WA, Chellappan K

Sensors (Basel), 2023 Mar 31;23(7).
PMID: 37050713 DOI: 10.3390/s23073654

Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG's signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments.

Matched MeSH terms: Electroencephalography/methods
Fulltext Electroencephalographic changes associated with antinociceptive actions of lidocaine, ketamine, meloxicam, and morphine administration in minimally anaesthetized dogs

Kaka U, Hui Cheng C, Meng GY, Fakurazi S, Kaka A, Behan AA, et al.

Biomed Res Int, 2015;2015:305367.
PMID: 25695060 DOI: 10.1155/2015/305367

Effects of ketamine and lidocaine on electroencephalographic (EEG) changes were evaluated in minimally anaesthetized dogs, subjected to electric stimulus. Six dogs were subjected to six treatments in a crossover design with a washout period of one week. Dogs were subjected to intravenous boluses of lidocaine 2 mg/kg, ketamine 3 mg/kg, meloxicam 0.2 mg/kg, morphine 0.2 mg/kg and loading doses of lidocaine 2 mg/kg followed by continuous rate infusion (CRI) of 50 and 100 mcg/kg/min, and ketamine 3 mg/kg followed by CRI of 10 and 50 mcg/kg/min. Electroencephalogram was recorded during electrical stimulation prior to any drug treatment (before treatment) and during electrical stimulation following treatment with the drugs (after treatment) under anaesthesia. Anaesthesia was induced with propofol and maintained with halothane at a stable concentration between 0.85 and 0.95%. Pretreatment median frequency was evidently increased (P < 0.05) for all treatment groups. Lidocaine, ketamine, and morphine depressed the median frequency resulting from the posttreatment stimulation. The depression of median frequency suggested evident antinociceptive effects of these treatments in dogs. It is therefore concluded that lidocaine and ketamine can be used in the analgesic protocol for the postoperative pain management in dogs.

Matched MeSH terms: Electroencephalography/methods
Fulltext Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework

Yahya N, Musa H, Ong ZY, Elamvazuthi I

Sensors (Basel), 2019 Nov 08;19(22).
PMID: 31717412 DOI: 10.3390/s19224878

In this work, an algorithm for the classification of six motor functions from an electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and a continuous wavelet transform (CWT), is investigated. The EEG data comprise six grasp-and-lift events, which are used to investigate the potential of using EEG as input signals with brain computer interface devices for controlling prosthetic devices for upper limb movement. Selected EEG channels are the ones located over the motor cortex, C3, Cz and C4, as well as at the parietal region, P3, Pz and P4. In general, the proposed algorithm includes three main stages, band pass filtering, CSP filtering, and wavelet transform and training on GoogLeNet for feature extraction, feature learning and classification. The band pass filtering is performed to select the EEG signal in the band of 7 Hz to 30 Hz while eliminating artifacts related to eye blink, heartbeat and muscle movement. The CSP filtering is applied on two-class EEG signals that will result in maximizing the power difference between the two-class dataset. Since CSP is mathematically developed for two-class events, the extension to the multiclass paradigm is achieved by using the approach of one class versus all other classes. Subsequently, continuous wavelet transform is used to convert the band pass and CSP filtered signals from selected electrodes to scalograms which are then converted to images in grayscale format. The three scalograms from the motor cortex regions and the parietal region are then combined to form two sets of RGB images. Next, these RGB images become the input to GoogLeNet for classification of the motor EEG signals. The performance of the proposed classification algorithm is evaluated in terms of precision, sensitivity, specificity, accuracy with average values of 94.8%, 93.5%, 94.7%, 94.1%, respectively, and average area under the receiver operating characteristic (ROC) curve equal to 0.985. These results indicate a good performance of the proposed algorithm in classifying grasp-and-lift events from EEG signals.

Matched MeSH terms: Electroencephalography/methods*
Fulltext Role of EEG as biomarker in the early detection and classification of dementia

Al-Qazzaz NK, Ali SH, Ahmad SA, Chellappan K, Islam MS, Escudero J

ScientificWorldJournal, 2014;2014:906038.
PMID: 25093211 DOI: 10.1155/2014/906038

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.

Matched MeSH terms: Electroencephalography/methods*
Context based error modeling for lossless compression of EEG signals using neural networks

Sriraam N, Eswaran C

J Med Syst, 2006 Dec;30(6):439-48.
PMID: 17233156

Two-stage lossless data compression methods involving predictors and encoders are well known. This paper discusses the application of context based error modeling techniques for neural network predictors used for the compression of EEG signals. Error modeling improves the performance of a compression algorithm by removing the statistical redundancy that exists among the error signals after the prediction stage. In this paper experiments are carried out by using human EEG signals recorded under various physiological conditions to evaluate the effect of context based error modeling in the EEG compression. It is found that the compression efficiency of the neural network based predictive techniques is significantly improved by using the error modeling schemes. It is shown that the bits per sample required for EEG compression with error modeling and entropy coding lie in the range of 2.92 to 6.62 which indicates a saving of 0.3 to 0.7 bits compared to the compression scheme without error modeling.

Matched MeSH terms: Electroencephalography/methods*

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