Displaying publications 21 - 40 of 245 in total

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  1. Fulltext The Decline of Cortical Beta Oscillation on the Function of Eye-hand Coordination in the Healthy Elderly
    Manor R, Cheaha D, Perimal E, Sathirapanya P, Kumarnsit E, Samerphob N
    In Vivo, 2023;37(4):1649-1657.
    PMID: 37369513 DOI: 10.21873/invivo.13250
    BACKGROUND/AIM: There seems to be a correlation between changes in movement patterns with aging and brain activation. In the preparation and execution of movements, neural oscillations play an important role. In this study, cortical high frequency brain oscillations were analyzed in 15 healthy young adults and 15 elderly adults who participated in eye-hand coordination tasks.

    PATIENTS AND METHODS: The brain activities of healthy young and older adults were recorded using electroencephalography (EEG).

    RESULTS: Elderly participants spent significantly more time completing the task than young participants. During eye-hand coordination in elderly groups, beta power decreased significantly in the central midline and parietal brain regions. The data suggest that healthy elderly subjects had intact cognitive performance, but relatively poor eye-hand coordination associated with loss of beta brain oscillation in the central midline and parietal cortex and reduced ability to attentional movement.

    CONCLUSION: Beta frequency in the parietal brain sites may contribute to attentional movement. This could be an important method for monitoring cognitive brain function changes as the brain ages.

    Matched MeSH terms: Electroencephalography*
  2. Fulltext Understanding mental fatigue and its detection: a comparative analysis of assessments and tools
    Kunasegaran K, Ismail AMH, Ramasamy S, Gnanou JV, Caszo BA, Chen PL
    PeerJ, 2023;11:e15744.
    PMID: 37637168 DOI: 10.7717/peerj.15744
    Mental fatigue has shown to be one of the root causes of decreased productivity and overall cognitive performance, by decreasing an individual's ability to inhibit responses, process information and concentrate. The effects of mental fatigue have led to occupational errors and motorway accidents. Early detection of mental fatigue can prevent the escalation of symptoms that may lead to chronic fatigue syndrome and other disorders. To date, in clinical settings, the assessment of mental fatigue and stress is done through self-reported questionnaires. The validity of these questionnaires is questionable, as they are highly subjective measurement tools and are not immune to response biases. This review examines the wider presence of mental fatigue in the general population and critically compares its various detection techniques (i.e., self-reporting questionnaires, heart rate variability, salivary cortisol levels, electroencephalogram, and saccadic eye movements). The ability of these detection tools to assess inhibition responses (which are sensitive enough to be manifested in a fatigue state) is specifically evaluated for a reliable marker in identifying mentally fatigued individuals. In laboratory settings, antisaccade tasks have been long used to assess inhibitory control and this technique can potentially serve as the most promising assessment tool to objectively detect mental fatigue. However, more studies need to be conducted in the future to validate and correlate this assessment with other existing measures of mental fatigue detection. This review is intended for, but not limited to, mental health professionals, digital health scientists, vision researchers, and behavioral scientists.
    Matched MeSH terms: Electroencephalography*
  3. Remote neurocognitive interventions for attention-deficit/hyperactivity disorder - Opportunities and challenges
    Zhang DW, Johnstone SJ, Sauce B, Arns M, Sun L, Jiang H
    Prog Neuropsychopharmacol Biol Psychiatry, 2023 Dec 20;127:110802.
    PMID: 37257770 DOI: 10.1016/j.pnpbp.2023.110802
    Improving neurocognitive functions through remote interventions has been a promising approach to developing new treatments for attention-deficit/hyperactivity disorder (AD/HD). Remote neurocognitive interventions may address the shortcomings of the current prevailing pharmacological therapies for AD/HD, e.g., side effects and access barriers. Here we review the current options for remote neurocognitive interventions to reduce AD/HD symptoms, including cognitive training, EEG neurofeedback training, transcranial electrical stimulation, and external cranial nerve stimulation. We begin with an overview of the neurocognitive deficits in AD/HD to identify the targets for developing interventions. The role of neuroplasticity in each intervention is then highlighted due to its essential role in facilitating neuropsychological adaptations. Following this, each intervention type is discussed in terms of the critical details of the intervention protocols, the role of neuroplasticity, and the available evidence. Finally, we offer suggestions for future directions in terms of optimizing the existing intervention protocols and developing novel protocols.
    Matched MeSH terms: Electroencephalography/methods
  4. Fulltext Resting-state electroencephalography (EEG) microstates of healthy individuals following mild sleep deprivation
    Khoo SY, Lai WH, On SH, On YY, Adam BM, Law WC, et al.
    Sci Rep, 2024 Jul 22;14(1):16820.
    PMID: 39039219 DOI: 10.1038/s41598-024-67902-0
    Mild sleep deprivation is widespread in many societies worldwide. Electroencephalography (EEG) microstate analysis provides information on spatial and temporal characteristics of resting brain network, serving as an indicator of neurophysiological activities at rest. This study seeks to investigate potential neural markers in EEG following mild sleep deprivation of a single night using EEG microstate analysis. Six-minute resting EEG was conducted on thirty healthy adults within 6 hours of waking in the morning and after at least 18 h of sleep deprivation. Translated and validated Malay language Karolinska Sleepiness Scale was used to assess the participants' degree of sleepiness. Microstate characteristics analysis was conducted on the final 24 subjects based on four standard microstate maps. Microstate C shows a significant increase in mean duration, coverage and occurrence, while microstate D has significantly higher occurrence after sleep deprivation. This study demonstrates notable changes in resting state EEG microstates following mild sleep deprivation. Present findings deepen our understanding of the brain's spatiotemporal dynamics under this condition and suggest the potential utility of neural markers in this domain as components of composite markers for sleep deprivation.
    Matched MeSH terms: Electroencephalography*
  5. 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/instrumentation*; Electroencephalography/methods*
  6. 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/instrumentation*; Electroencephalography/methods
  7. 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*
  8. Fulltext Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization
    Adam A, Shapiai MI, Tumari MZ, Mohamad MS, Mubin M
    ScientificWorldJournal, 2014;2014:973063.
    PMID: 25243236 DOI: 10.1155/2014/973063
    Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.
    Matched MeSH terms: Electroencephalography/methods*
  9. Fulltext The electroencephalogram changes in migraineurs
    Tan HJ, Suganthi C, Dhachayani S, Rizal AM, Raymond AA
    Med J Malaysia, 2007 Mar;62(1):56-8.
    PMID: 17682573 MyJurnal
    Migraine is associated with a variety of electroencephalographic (EEG) changes. Previous studies using analogue EEG and old diagnostic criteria may under or over report the prevalence of EEG changes in migraine. The objective of this study was to reevaluate the EEG changes in migraine patients diagnosed by applying the new International Classification of Headache Disorder -2 criteria. This was a case control study involving 70 migraine patients and 70 age and gender matched control who were subjected to scalp EEG. The EEG changes during hyperventilation (HV), which were significantly more common in the migraine group were theta activity (34 vs 22, p = 0.038) and frontal intermittent rhythmic delta activity (FIRDA) (10 vs 3, p = 0.042). Applying the new ICHD -2 diagnostic criteria and digital EEG, this study yielded previously unrecognized features including FIRDA during HV on EEG.
    Matched MeSH terms: Electroencephalography*
  10. 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*
  11. Deep convolutional neural network for classification of sleep stages from single-channel EEG signals
    Mousavi Z, Yousefi Rezaii T, Sheykhivand S, Farzamnia A, Razavi SN
    J Neurosci Methods, 2019 08 01;324:108312.
    PMID: 31201824 DOI: 10.1016/j.jneumeth.2019.108312
    Using a smart method for automatic diagnosis in medical applications, such as sleep stage classification is considered as one of the important challenges of the last few years which can replace the time-consuming process of visual inspection done by specialists. One of the problems regarding the automatic diagnosis of sleep patterns is extraction and selection of discriminative features generally demanding high computational burden. This paper provides a new single-channel approach to automatic classification of sleep stages from EEG signal. The main idea is to directly apply the raw EEG signal to deep convolutional neural network, without involving feature extraction/selection, which is a challenging process in the previous literature. The proposed network architecture includes 9 convolutional layers followed by 2 fully connected layers. In order to make the samples of different classes balanced, we used a preprocessing method called data augmentation. The simulation results of the proposed method for classification of 2 to 6 classes of sleep stages show the accuracy of 98.10%, 96.86%, 93.11%, 92.95%, 93.55% and Cohen's Kappa coefficient of 0.98%, 0.94%, 0.90%, 0.86% and 0.89%, respectively. Furthermore, comparing the obtained results with the state-of-the-art methods reveals the performance improvement of the proposed sleep stage classification in terms of accuracy and Cohen's Kappa coefficient.
    Matched MeSH terms: Electroencephalography/methods*
  12. Fulltext EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features
    Hag A, Handayani D, Pillai T, Mantoro T, Kit MH, Al-Shargie F
    Sensors (Basel), 2021 Sep 20;21(18).
    PMID: 34577505 DOI: 10.3390/s21186300
    Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.
    Matched MeSH terms: Electroencephalography*
  13. A Systematic Review for Human EEG Brain Signals Based Emotion Classification, Feature Extraction, Brain Condition, Group Comparison
    Hamada M, Zaidan BB, Zaidan AA
    J Med Syst, 2018 Jul 24;42(9):162.
    PMID: 30043178 DOI: 10.1007/s10916-018-1020-8
    The study of electroencephalography (EEG) signals is not a new topic. However, the analysis of human emotions upon exposure to music considered as important direction. Although distributed in various academic databases, research on this concept is limited. To extend research in this area, the researchers explored and analysed the academic articles published within the mentioned scope. Thus, in this paper a systematic review is carried out to map and draw the research scenery for EEG human emotion into a taxonomy. Systematically searched all articles about the, EEG human emotion based music in three main databases: ScienceDirect, Web of Science and IEEE Xplore from 1999 to 2016. These databases feature academic studies that used EEG to measure brain signals, with a focus on the effects of music on human emotions. The screening and filtering of articles were performed in three iterations. In the first iteration, duplicate articles were excluded. In the second iteration, the articles were filtered according to their titles and abstracts, and articles outside of the scope of our domain were excluded. In the third iteration, the articles were filtered by reading the full text and excluding articles outside of the scope of our domain and which do not meet our criteria. Based on inclusion and exclusion criteria, 100 articles were selected and separated into five classes. The first class includes 39 articles (39%) consists of emotion, wherein various emotions are classified using artificial intelligence (AI). The second class includes 21 articles (21%) is composed of studies that use EEG techniques. This class is named 'brain condition'. The third class includes eight articles (8%) is related to feature extraction, which is a step before emotion classification. That this process makes use of classifiers should be noted. However, these articles are not listed under the first class because these eight articles focus on feature extraction rather than classifier accuracy. The fourth class includes 26 articles (26%) comprises studies that compare between or among two or more groups to identify and discover human emotion-based EEG. The final class includes six articles (6%) represents articles that study music as a stimulus and its impact on brain signals. Then, discussed the five main categories which are action types, age of the participants, and number size of the participants, duration of recording and listening to music and lastly countries or authors' nationality that published these previous studies. it afterward recognizes the main characteristics of this promising area of science in: motivation of using EEG process for measuring human brain signals, open challenges obstructing employment and recommendations to improve the utilization of EEG process.
    Matched MeSH terms: Electroencephalography*
  14. Fulltext Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm
    Hag A, Handayani D, Altalhi M, Pillai T, Mantoro T, Kit MH, et al.
    Sensors (Basel), 2021 Dec 15;21(24).
    PMID: 34960469 DOI: 10.3390/s21248370
    In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.
    Matched MeSH terms: Electroencephalography*
  15. Fulltext EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications
    Abdi Alkareem Alyasseri Z, Alomari OA, Al-Betar MA, Awadallah MA, Hameed Abdulkareem K, Abed Mohammed M, et al.
    Comput Intell Neurosci, 2022;2022:5974634.
    PMID: 35069721 DOI: 10.1155/2022/5974634
    Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain's electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.
    Matched MeSH terms: Electroencephalography*
  16. Fulltext Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion
    Zafar R, Dass SC, Malik AS
    PLoS One, 2017;12(5):e0178410.
    PMID: 28558002 DOI: 10.1371/journal.pone.0178410
    Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.
    Matched MeSH terms: Electroencephalography/methods*
  17. 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
  18. 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
  19. Fulltext Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM
    Lai CQ, Ibrahim H, Abd Hamid AI, Abdullah JM
    Sensors (Basel), 2020 Sep 14;20(18).
    PMID: 32937801 DOI: 10.3390/s20185234
    Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most frequently submitted insurance claims. However, it is often always misused when individuals attempt an insurance fraud claim by providing false medical conditions. Therefore, there is a need for an instant brain condition classification system. This study presents a novel classification architecture that can classify non-severe TBI patients and healthy subjects employing resting-state electroencephalogram (EEG) as the input, solving the immobility issue of the computed tomography (CT) scan and magnetic resonance imaging (MRI). The proposed architecture makes use of long short term memory (LSTM) and error-correcting output coding support vector machine (ECOC-SVM) to perform multiclass classification. The pre-processed EEG time series are supplied to the network by each time step, where important information from the previous time step will be remembered by the LSTM cell. Activations from the LSTM cell is used to train an ECOC-SVM. The temporal advantages of the EEG were amplified and able to achieve a classification accuracy of 100%. The proposed method was compared to existing works in the literature, and it is shown that the proposed method is superior in terms of classification accuracy, sensitivity, specificity, and precision.
    Matched MeSH terms: Electroencephalography*
  20. A premium for positive social interest and attractive voices in the acceptability of unfair offers? An ERP study
    Yuan Y, Shang J, Gao C, Sommer W, Li W
    Eur J Neurosci, 2024 Jul;60(2):4078-4094.
    PMID: 38777332 DOI: 10.1111/ejn.16422
    Although the attractiveness of voices plays an important role in social interactions, it is unclear how voice attractiveness and social interest influence social decision-making. Here, we combined the ultimatum game with recording event-related brain potentials (ERPs) and examined the effect of attractive versus unattractive voices of the proposers, expressing positive versus negative social interest ("I like you" vs. "I don't like you"), on the acceptance of the proposal. Overall, fair offers were accepted at significantly higher rates than unfair offers, and high voice attractiveness increased acceptance rates for all proposals. In ERPs in response to the voices, their attractiveness and expressed social interests yielded early additive effects in the N1 component, followed by interactions in the subsequent P2, P3 and N400 components. More importantly, unfair offers elicited a larger Medial Frontal Negativity (MFN) than fair offers but only when the proposer's voice was unattractive or when the voice carried positive social interest. These results suggest that both voice attractiveness and social interest moderate social decision-making and there is a similar "beauty premium" for voices as for faces.
    Matched MeSH terms: Electroencephalography/methods
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