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Tracking of EEG activity using motion estimation to understand brain wiring

Nisar H, Malik AS, Ullah R, Shim SO, Bawakid A, Khan MB, et al.

Adv Exp Med Biol, 2015;823:159-74.
PMID: 25381107 DOI: 10.1007/978-3-319-10984-8_9

The fundamental step in brain research deals with recording electroencephalogram (EEG) signals and then investigating the recorded signals quantitatively. Topographic EEG (visual spatial representation of EEG signal) is commonly referred to as brain topomaps or brain EEG maps. In this chapter, full search full search block motion estimation algorithm has been employed to track the brain activity in brain topomaps to understand the mechanism of brain wiring. The behavior of EEG topomaps is examined throughout a particular brain activation with respect to time. Motion vectors are used to track the brain activation over the scalp during the activation period. Using motion estimation it is possible to track the path from the starting point of activation to the final point of activation. Thus it is possible to track the path of a signal across various lobes.

Matched MeSH terms: Electroencephalography/methods*
A generalized subspace approach for estimating visual evoked potentials

Kamel N, Yusoff MZ

Annu Int Conf IEEE Eng Med Biol Soc, 2008;2008:5208-11.
PMID: 19163891 DOI: 10.1109/IEMBS.2008.4650388

A "single-trial" signal subspace approach for extracting visual evoked potential (VEP) from the ongoing 'colored' electroencephalogram (EEG) noise is proposed. The algorithm applies the generalized eigendecomposition on the covariance matrices of the VEP and noise to transform them jointly into diagonal matrices in order to avoid a pre-whitening stage. The proposed generalized subspace approach (GSA) decomposes the corrupted VEP space into a signal subspace and noise subspace. Enhancement is achieved by removing the noise subspace and estimating the clean VEPs only from the signal subspace. The validity and effectiveness of the proposed GSA scheme in estimating the latencies of P100's (used in objective assessment of visual pathways) are evaluated using real data collected from Selayang Hospital in Kuala Lumpur. The performance of GSA is compared with the recently proposed single-trial technique called the Third Order Correlation (TOC).

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*
Association of autonomic nervous system and EEG scalp potential during playing 2D Grand Turismo 5

Subhani AR, Likun X, Saeed Malik A

Annu Int Conf IEEE Eng Med Biol Soc, 2012;2012:3420-3.
PMID: 23366661 DOI: 10.1109/EMBC.2012.6346700

Cerebral activation and autonomic nervous system have importance in studies such as mental stress. The aim of this study is to analyze variations in EEG scalp potential which may influence autonomic activation of heart while playing video games. Ten healthy participants were recruited in this study. Electroencephalogram (EEG) and electrocardiogram (ECG) signals were measured simultaneously during playing video game and rest conditions. Sympathetic and parasympathetic innervations of heart were evaluated from heart rate variability (HRV), derived from the ECG. Scalp potential was measured by the EEG. The results showed a significant upsurge in the value theta Fz/alpha Pz (p<0.001) while playing game. The results also showed tachycardia while playing video game as compared to rest condition (p<0.005). Normalized low frequency power and ratio of low frequency/high frequency power were significantly increased while playing video game and normalized high frequency power sank during video games. Results showed synchronized activity of cerebellum and sympathetic and parasympathetic innervation of heart.

Matched MeSH terms: Electroencephalography/methods*
An expectation-maximization algorithm based Kalman smoother approach for single-trial estimation of event-related potentials

Ting CM, Samdin SB, Salleh ShH, Omar MH, Kamarulafizam I

Annu Int Conf IEEE Eng Med Biol Soc, 2012;2012:6534-8.
PMID: 23367426 DOI: 10.1109/EMBC.2012.6347491

This paper applies an expectation-maximization (EM) based Kalman smoother (KS) approach for single-trial event-related potential (ERP) estimation. Existing studies assume a Markov diffusion process for the dynamics of ERP parameters which is recursively estimated by optimal filtering approaches such as Kalman filter (KF). However, these studies only consider estimation of ERP state parameters while the model parameters are pre-specified using manual tuning, which is time-consuming for practical usage besides giving suboptimal estimates. We extend the KF approach by adding EM based maximum likelihood estimation of the model parameters to obtain more accurate ERP estimates automatically. We also introduce different model variants by allowing flexibility in the covariance structure of model noises. Optimal model selection is performed based on Akaike Information Criterion (AIC). The method is applied to estimation of chirp-evoked auditory brainstem responses (ABRs) for detection of wave V critical for assessment of hearing loss. Results shows that use of more complex covariances are better estimating inter-trial variability.

Matched MeSH terms: Electroencephalography/methods*
EEG spectral analysis on Muslim prayers

Doufesh H, Faisal T, Lim KS, Ibrahim F

Appl Psychophysiol Biofeedback, 2012 Mar;37(1):11-8.
PMID: 21965118 DOI: 10.1007/s10484-011-9170-1

This study investigated the proposition of relaxation offered by performing the Muslim prayers by measuring the alpha brain activity in the frontal (F3-F4), central (C3-C4), parietal (P3-P4), and occipital (O1-O2) electrode placements using the International 10-20 System. Nine Muslim subjects were asked to perform the four required cycles of movements of Dhuha prayer, and the EEG were subsequently recorded with open eyes under three conditions, namely, resting, performing four cycles of prayer while reciting the specific verses and supplications, and performing four cycles of acted salat condition (prayer movements without any recitations). Analysis of variance (ANOVA) tests revealed that there were no significant difference in the mean alpha relative power (RP(α)) between the alpha amplitude in the Dhuha prayer and the acted conditions in all eight electrode positions. However, the mean RP(α) showed higher alpha amplitude during the prostration position of the Dhuha prayer and acted condition at the parietal and occipital regions in comparison to the resting condition. Findings were similar to other studies documenting increased alpha amplitude in parietal and occipital regions during meditation and mental concentration. The incidence of increased alpha amplitude suggested parasympathetic activation, thus indicating a state of relaxation. Subsequent studies are needed to delineate the role of mental concentration, and eye focus, on alpha wave amplitude while performing worshipping acts.

Matched MeSH terms: Electroencephalography/methods*
An EEG-based functional connectivity measure for automatic detection of alcohol use disorder

Mumtaz W, Saad MNBM, Kamel N, Ali SSA, Malik AS

Artif Intell Med, 2018 01;84:79-89.
PMID: 29169647 DOI: 10.1016/j.artmed.2017.11.002

BACKGROUND: The abnormal alcohol consumption could cause toxicity and could alter the human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately, the conventional screening methods for AUD patients are subjective and manual. Hence, to perform automatic screening of AUD patients, objective methods are needed. The electroencephalographic (EEG) data have been utilized to study the differences of brain signals between alcoholics and healthy controls that could further developed as an automatic screening tool for alcoholics.
METHOD: In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were used.
RESULTS: The study resulted into SVM classification accuracy=98%, sensitivity=99.9%, specificity=95%, and f-measure=0.97; LR classification accuracy=91.7%, sensitivity=86.66%, specificity=96.6%, and f-measure=0.90; NB classification accuracy=93.6%, sensitivity=100%, specificity=87.9%, and f-measure=0.95.
CONCLUSION: The SL features could be utilized as objective markers to screen the AUD patients and healthy controls.

Matched MeSH terms: Electroencephalography/methods*
EEG based brain source localization comparison of sLORETA and eLORETA

Jatoi MA, Kamel N, Malik AS, Faye I

Australas Phys Eng Sci Med, 2014 Dec;37(4):713-21.
PMID: 25359588 DOI: 10.1007/s13246-014-0308-3

Human brain generates electromagnetic signals during certain activation inside the brain. The localization of the active sources which are responsible for such activation is termed as brain source localization. This process of source estimation with the help of EEG which is also known as EEG inverse problem is helpful to understand physiological, pathological, mental, functional abnormalities and cognitive behaviour of the brain. This understanding leads for the specification for diagnoses of various brain disorders such as epilepsy and tumour. Different approaches are devised to exactly localize the active sources with minimum localization error, less complexity and more validation which include minimum norm, low resolution brain electromagnetic tomography (LORETA), standardized LORETA, exact LORETA, Multiple Signal classifier, focal under determined system solution etc. This paper discusses and compares the ability of localizing the sources for two low resolution methods i.e., sLORETA and eLORETA respectively. The ERP data with visual stimulus is used for comparison at four different time instants for both methods (sLORETA and eLORETA) and then corresponding activation in terms of scalp map, slice view and cortex map is discussed.

Matched MeSH terms: Electroencephalography/methods*
Drowsiness detection during different times of day using multiple features

Sahayadhas A, Sundaraj K, Murugappan M

Australas Phys Eng Sci Med, 2013 Jun;36(2):243-50.
PMID: 23719977 DOI: 10.1007/s13246-013-0200-6

Driver drowsiness has been one of the major causes of road accidents that lead to severe trauma, such as physical injury, death, and economic loss, which highlights the need to develop a system that can alert drivers of their drowsy state prior to accidents. Researchers have therefore attempted to develop systems that can determine driver drowsiness using the following four measures: (1) subjective ratings from drivers, (2) vehicle-based measures, (3) behavioral measures and (4) physiological measures. In this study, we analyzed the various factors that contribute towards drowsiness. A total of 15 male subjects were asked to drive for 2 h at three different times of the day (00:00-02:00, 03:00-05:00 and 15:00-17:00 h) when the circadian rhythm is low. The less intrusive physiological signal measurements, ECG and EMG, are analyzed during this driving task. Statistically significant differences in the features of ECG and sEMG signals were observed between the alert and drowsy states of the drivers during different times of day. In the future, these physiological measures can be fused with vision-based measures for the development of an efficient drowsiness detection system.

Matched MeSH terms: Electroencephalography/methods*
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*
Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal

Namazi H, Akrami A, Nazeri S, Kulish VV

Biomed Res Int, 2016;2016:5469587.
PMID: 27699169

An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal EEG signal. Also, odorant having higher entropy causes the EEG signal to have lower approximate entropy. The method discussed here can be applied and investigated in case of patients with brain diseases as the rehabilitation purpose.

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
A Comparative Study of Different EEG Reference Choices for Diagnosing Unipolar Depression

Mumtaz W, Malik AS

Brain Topogr, 2018 09;31(5):875-885.
PMID: 29860588 DOI: 10.1007/s10548-018-0651-x

The choice of an electroencephalogram (EEG) reference has fundamental importance and could be critical during clinical decision-making because an impure EEG reference could falsify the clinical measurements and subsequent inferences. In this research, the suitability of three EEG references was compared while classifying depressed and healthy brains using a machine-learning (ML)-based validation method. In this research, the EEG data of 30 unipolar depressed subjects and 30 age-matched healthy controls were recorded. The EEG data were analyzed in three different EEG references, the link-ear reference (LE), average reference (AR), and reference electrode standardization technique (REST). The EEG-based functional connectivity (FC) was computed. Also, the graph-based measures, such as the distances between nodes, minimum spanning tree, and maximum flow between the nodes for each channel pair, were calculated. An ML scheme provided a mechanism to compare the performances of the extracted features that involved a general framework such as the feature extraction (graph-based theoretic measures), feature selection, classification, and validation. For comparison purposes, the performance metrics such as the classification accuracies, sensitivities, specificities, and F scores were computed. When comparing the three references, the diagnostic accuracy showed better performances during the REST, while the LE and AR showed less discrimination between the two groups. Based on the results, it can be concluded that the choice of appropriate reference is critical during the clinical scenario. The REST reference is recommended for future applications of EEG-based diagnosis of mental illnesses.

Matched MeSH terms: Electroencephalography/methods*
Novel Multipin Electrode Cap System for Dry Electroencephalography

Fiedler P, Pedrosa P, Griebel S, Fonseca C, Vaz F, Supriyanto E, et al.

Brain Topogr, 2015 Sep;28(5):647-656.
PMID: 25998854 DOI: 10.1007/s10548-015-0435-5

Current usage of electroencephalography (EEG) is limited to laboratory environments. Self-application of a multichannel wet EEG caps is practically impossible, since the application of state-of-the-art wet EEG sensors requires trained laboratory staff. We propose a novel EEG cap system with multipin dry electrodes overcoming this problem. We describe the design of a novel 24-pin dry electrode made from polyurethane and coated with Ag/AgCl. A textile cap system holds 97 of these dry electrodes. An EEG study with 20 volunteers compares the 97-channel dry EEG cap with a conventional 128-channel wet EEG cap for resting state EEG, alpha activity, eye blink artifacts and checkerboard pattern reversal visual evoked potentials. All volunteers report a good cap fit and good wearing comfort. Average impedances are below 150 kΩ for 92 out of 97 dry electrodes, enabling recording with standard EEG amplifiers. No significant differences are observed between wet and dry power spectral densities for all EEG bands. No significant differences are observed between the wet and dry global field power time courses of visual evoked potentials. The 2D interpolated topographic maps show significant differences of 3.52 and 0.44% of the map areas for the N75 and N145 VEP components, respectively. For the P100 component, no significant differences are observed. Dry multipin electrodes integrated in a textile EEG cap overcome the principle limitations of wet electrodes, allow rapid application of EEG multichannel caps by non-trained persons, and thus enable new fields of application for multichannel EEG acquisition.

Matched MeSH terms: Electroencephalography/methods
Fulltext Robust EEG/MEG Based Functional Connectivity with the Envelope of the Imaginary Coherence: Sensor Space Analysis

Sanchez Bornot JM, Wong-Lin K, Ahmad AL, Prasad G

Brain Topogr, 2018 11;31(6):895-916.
PMID: 29546509 DOI: 10.1007/s10548-018-0640-0

The brain's functional connectivity (FC) estimated at sensor level from electromagnetic (EEG/MEG) signals can provide quick and useful information towards understanding cognition and brain disorders. Volume conduction (VC) is a fundamental issue in FC analysis due to the effects of instantaneous correlations. FC methods based on the imaginary part of the coherence (iCOH) of any two signals are readily robust to VC effects, but neglecting the real part of the coherence leads to negligible FC when the processes are truly connected but with zero or π-phase (modulus 2π) interaction. We ameliorate this issue by proposing a novel method that implements an envelope of the imaginary coherence (EIC) to approximate the coherence estimate of supposedly active underlying sources. We compare EIC with state-of-the-art FC measures that included lagged coherence, iCOH, phase lag index (PLI) and weighted PLI (wPLI), using bivariate autoregressive and stochastic neural mass models. Additionally, we create realistic simulations where three and five regions were mapped on a template cortical surface and synthetic MEG signals were obtained after computing the electromagnetic leadfield. With this simulation and comparison study, we also demonstrate the feasibility of sensor FC analysis using receiver operating curve analysis whilst varying the signal's noise level. However, these results should be interpreted with caution given the known limitations of the sensor-based FC approach. Overall, we found that EIC and iCOH demonstrate superior results with most accurate FC maps. As they complement each other in different scenarios, that will be important to study normal and diseased brain activity.

Matched MeSH terms: Electroencephalography/methods*
Fulltext An EEG Database and Its Initial Benchmark Emotion Classification Performance

Seal A, Reddy PPN, Chaithanya P, Meghana A, Jahnavi K, Krejcar O, et al.

Comput Math Methods Med, 2020;2020:8303465.
PMID: 32831902 DOI: 10.1155/2020/8303465

Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications. However, most of the state-of-the-art methods identified emotions after analyzing facial images. Emotion recognition using electroencephalogram (EEG) signals has got less attention. However, the advantage of using EEG signals is that it can capture real emotion. However, very few EEG signals databases are publicly available for affective computing. In this work, we present a database consisting of EEG signals of 44 volunteers. Twenty-three out of forty-four are females. A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos. So, 3 video files are devoted to each emotion. Participants are mapped with the emotion that they had felt after watching each video. The recorded EEG signals are considered further to classify four types of emotions based on discrete wavelet transform and extreme learning machine (ELM) for reporting the initial benchmark classification performance. The ELM algorithm is used for channel selection followed by subband selection. The proposed method performs the best when features are captured from the gamma subband of the FP1-F7 channel with 94.72% accuracy. The presented database would be available to the researchers for affective recognition applications.

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
Complexity-based decoding of brain-skin relation in response to olfactory stimuli

Omam S, Babini MH, Sim S, Tee R, Nathan V, Namazi H

Comput Methods Programs Biomed, 2020 Feb;184:105293.
PMID: 31887618 DOI: 10.1016/j.cmpb.2019.105293

BACKGROUND AND OBJECTIVE: Human body is covered with skin in different parts. In fact, skin reacts to different changes around human. For instance, when the surrounding temperature changes, human skin will react differently. It is known that the activity of skin is regulated by human brain. In this research, for the first time we investigate the relation between the activities of human skin and brain by mathematical analysis of Galvanic Skin Response (GSR) and Electroencephalography (EEG) signals.
METHOD: For this purpose, we employ fractal theory and analyze the variations of fractal dimension of GSR and EEG signals when subjects are exposed to different olfactory stimuli in the form of pleasant odors.
RESULTS: Based on the obtained results, the complexity of GSR signal changes with the complexity of EEG signal in case of different stimuli, where by increasing the molecular complexity of olfactory stimuli, the complexity of EEG and GSR signals increases. The results of statistical analysis showed the significant effect of stimulation on variations of complexity of GSR signal. In addition, based on effect size analysis, fourth odor with greatest molecular complexity had the greatest effect on variations of complexity of EEG and GSR signals.
CONCLUSION: Therefore, it can be said that human skin reaction changes with the variations in the activity of human brain. The result of analysis in this research can be further used to make a model between the activities of human skin and brain that will enable us to predict skin reaction to different stimuli.

Matched MeSH terms: Electroencephalography/methods
Automatic seizure detection by convolutional neural networks with computational complexity analysis

Cimr D, Fujita H, Tomaskova H, Cimler R, Selamat A

Comput Methods Programs Biomed, 2023 Feb;229:107277.
PMID: 36463672 DOI: 10.1016/j.cmpb.2022.107277

BACKGROUND AND OBJECTIVES: Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure detection. We designed a CAD system approach for seizure detection that optimizes the complexity of the required solution while also being reusable on different problems.
METHODS: The methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network.
RESULTS: Depending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset.
CONCLUSIONS: Through the approach to detection, the system offers an optimized solution for seizure diagnosis health problems. The proposed solution should be implemented in all clinical or home environments for decision support.

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*

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