Displaying publications 1 - 20 of 247 in total

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  1. 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*
  2. Satar SNA, Mogan S, Jaafar WPN, Maghalingam S, Affendi FAR, Ng CF, et al.
    Med J Malaysia, 2023 Mar;78(2):149-154.
    PMID: 36988523
    INTRODUCTION: Electroencephalogram (EEG) is an important investigational tool that is widely used in the hospital settings for numerous indications. The aim was to determine factors associated with abnormal EEG and its clinical correlations in hospitalised patients.

    MATERIALS AND METHODS: Patients with at least one EEG recording were recruited. The EEG and clinical data were collated.

    RESULTS: Two hundred and fifty patients underwent EEG and 154 (61.6%) were found to have abnormal EEG. The abnormal changes consist of theta activity (79,31.6%), delta activity (20, 8%), focal discharges (41,16.4%) and generalised discharges (14, 5.6%). Older patients had 3.481 higher risk for EEG abnormalities, p=0.001. Patients who had focal seizures had 2.240 higher risk of having EEG abnormalities, p<0.001. Low protein level was a risk for EEG abnormalities, p=0.003.

    CONCLUSION: This study emphasised that an abnormal EEG remains a useful tool in determining the likelihood for seizures in a hospital setting. The risk factors for EEG abnormality in hospitalised patients were age, focal seizures and low protein level. The EEG may have an important role as part of the workup in hospitalised patients to aid the clinician to tailor their management in a holistic manner.

    Matched MeSH terms: Electroencephalography*
  3. Abualsaud K, Mahmuddin M, Saleh M, Mohamed A
    ScientificWorldJournal, 2015;2015:945689.
    PMID: 25759863 DOI: 10.1155/2015/945689
    Brain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities. This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals. This noise-aware signal combination (NSC) ensemble classifier combines four classification models based on their individual performance. The main objective of the proposed classifier is to enhance the classification accuracy in the presence of noisy and incomplete information while preserving a reasonable amount of complexity. The experimental results show the effectiveness of the NSC technique, which yields higher accuracies of 90% for noiseless data compared with 85%, 85.9%, and 89.5% in other experiments. The accuracy for the proposed method is 80% when SNR=1 dB, 84% when SNR=5 dB, and 88% when SNR=10 dB, while the compression ratio (CR) is 85.35% for all of the datasets mentioned.
    Matched MeSH terms: Electroencephalography/methods*
  4. Babiker A, Faye I
    Comput Intell Neurosci, 2021;2021:6617462.
    PMID: 33564299 DOI: 10.1155/2021/6617462
    Situational interest (SI) is one of the promising states that can improve student's learning and increase the acquired knowledge. Electroencephalogram- (EEG-) based detection of SI could assist in understanding SI neuroscientific causes that, as a result, could explain the SI role in student's learning. In this study, 26 participants were selected based on questionnaires to participate in the mathematics classroom experiment. SI and personal interest (PI) questionnaires along with knowledge tests were undertaken to measure student's interest and knowledge levels. A hybrid method combining empirical mode decomposition (EMD) and wavelet transform was developed and employed for feature extraction. The proposed method showed significant difference using the multivariate analysis of variance (MANOVA) test and consistently outperformed other methods in the classification performance using weighted k-nearest neighbours (wkNN). The high classification accuracy of 85.7% with the sensitivity of 81.8% and specificity of 90% revealed that brain oscillation patterns of high SI students are somewhat different than students with low or no SI. In addition, the result suggests that the delta rhythm could have a significant effect on cognitive processing.
    Matched MeSH terms: Electroencephalography*
  5. Soundirarajan M, Pakniyat N, Sim S, Nathan V, Namazi H
    Technol Health Care, 2021;29(1):99-109.
    PMID: 32568131 DOI: 10.3233/THC-192085
    BACKGROUND: Human facial muscles react differently to different visual stimuli. It is known that the human brain controls and regulates the activity of the muscles.

    OBJECTIVE: In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain.

    METHODS: Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content).

    RESULTS: Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals.

    CONCLUSION: This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.

    Matched MeSH terms: Electroencephalography*
  6. Kamal SM, Dawi NM, Namazi H
    Technol Health Care, 2021;29(6):1109-1118.
    PMID: 33749623 DOI: 10.3233/THC-202744
    BACKGROUND: Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements.

    OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view.

    METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents.

    RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.

    Matched MeSH terms: Electroencephalography*
  7. Pavlov YG, Adamian N, Appelhoff S, Arvaneh M, Benwell CSY, Beste C, et al.
    Cortex, 2021 11;144:213-229.
    PMID: 33965167 DOI: 10.1016/j.cortex.2021.03.013
    There is growing awareness across the neuroscience community that the replicability of findings about the relationship between brain activity and cognitive phenomena can be improved by conducting studies with high statistical power that adhere to well-defined and standardised analysis pipelines. Inspired by recent efforts from the psychological sciences, and with the desire to examine some of the foundational findings using electroencephalography (EEG), we have launched #EEGManyLabs, a large-scale international collaborative replication effort. Since its discovery in the early 20th century, EEG has had a profound influence on our understanding of human cognition, but there is limited evidence on the replicability of some of the most highly cited discoveries. After a systematic search and selection process, we have identified 27 of the most influential and continually cited studies in the field. We plan to directly test the replicability of key findings from 20 of these studies in teams of at least three independent laboratories. The design and protocol of each replication effort will be submitted as a Registered Report and peer-reviewed prior to data collection. Prediction markets, open to all EEG researchers, will be used as a forecasting tool to examine which findings the community expects to replicate. This project will update our confidence in some of the most influential EEG findings and generate a large open access database that can be used to inform future research practices. Finally, through this international effort, we hope to create a cultural shift towards inclusive, high-powered multi-laboratory collaborations.
    Matched MeSH terms: Electroencephalography*
  8. 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
  9. Finsterer J
    Med J Malaysia, 2023 May;78(3):421-426.
    PMID: 37271853
    OBJECTIVES: Severe, acute, respiratory syndromecoronavirus- 2 (SARS-CoV-2) infections can be complicated by central nervous system (CNS) disease. One of the CNS disorders associated with Coronavirus Disease-19 (COVID- 19) is posterior reversible encephalopathy syndrome (PRES). This narrative review summarises and discusses previous and recent findings on SARS-CoV-2 associated PRES.

    METHODS: A literature search was carried out in PubMed and Google Scholar using suitable search terms and reference lists of articles found were searched for further articles.

    RESULTS: By the end of February 2023, 82 patients with SARS-CoV-2 associated PRES were recorded. The latency between the onset of COVID-19 and the onset of PRES ranged from 1 day to 70 days. The most common presentations of PRES were mental deterioration (n=47), seizures (n=46) and visual disturbances (n=18). Elevated blood pressure was reported on admission or during hospitalisation in 48 patients. The most common comorbidities were arterial hypertension, diabetes, hyperlipidemia and atherosclerosis. PRES was best diagnosed by multimodal cerebral magnetic resonance imaging (MRI). Complete recovery was reported in 35 patients and partial recovery in 21 patients, while seven patients died.

    CONCLUSIONS: PRES can be a CNS complication associated with COVID-19. COVID-19 patients with mental dysfunction, seizures or visual disturbances should immediately undergo CNS imaging through multimodal MRI, electroencephalography (EEG) and cerebrospinal fluid (CSF) studies in order not to miss PRES.

    Matched MeSH terms: Electroencephalography/adverse effects; Electroencephalography/methods
  10. 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*; Electroencephalography Phase Synchronization/physiology*
  11. Kang X, Handayani DOD, Chong PP, Acharya UR
    Comput Biol Med, 2020 10;125:103970.
    PMID: 32892114 DOI: 10.1016/j.compbiomed.2020.103970
    Nowadays human behavior has been affected with the advent of new digital technologies. Due to the rampant use of the Internet by children, many have been addicted to pornography. This addiction has negatively affected the behaviors of children including increased impulsiveness, learning ability to attention, poor decision-making, memory problems, and deficit in emotion regulation. The children with porn addiction can be identified by parents and medical practitioners as third-party observers. This systematic literature review (SLR) is conducted to increase the understanding of porn addiction using electroencephalogram (EEG) signals. We have searched five different databases namely IEEE, ACM, Science Direct, Springer and National Center for Biotechnology Information (NCBI) using addiction, porn, and EEG as keywords along with 'OR 'operation in between the expressions. We have selected 46 studies in this work by screening 815,554 papers from five databases. Our results show that it is possible to identify children with porn addiction using EEG signals.
    Matched MeSH terms: Electroencephalography
  12. Abu Hasan R, Sulaiman S, Ashykin NN, Abdullah MN, Hafeez Y, Ali SSA
    Sensors (Basel), 2021 Jul 18;21(14).
    PMID: 34300624 DOI: 10.3390/s21144885
    Adults are constantly exposed to stressful conditions at their workplace, and this can lead to decreased job performance followed by detrimental clinical health problems. Advancement of sensor technologies has allowed the electroencephalography (EEG) devices to be portable and used in real-time to monitor mental health. However, real-time monitoring is not often practical in workplace environments with complex operations such as kindergarten, firefighting and offshore facilities. Integrating the EEG with virtual reality (VR) that emulates workplace conditions can be a tool to assess and monitor mental health of adults within their working environment. This paper evaluates the mental states induced when performing a stressful task in a VR-based offshore environment. The theta, alpha and beta frequency bands are analysed to assess changes in mental states due to physical discomfort, stress and concentration. During the VR trials, mental states of discomfort and disorientation are observed with the drop of theta activity, whilst the stress induced from the conditional tasks is reflected in the changes of low-alpha and high-beta activities. The deflection of frontal alpha asymmetry from negative to positive direction reflects the learning effects from emotion-focus to problem-solving strategies adopted to accomplish the VR task. This study highlights the need for an integrated VR-EEG system in workplace settings as a tool to monitor and assess mental health of working adults.
    Matched MeSH terms: Electroencephalography
  13. Zafar R, Qayyum A, Mumtaz W
    J Integr Neurosci, 2019 Sep 30;18(3):217-229.
    PMID: 31601069 DOI: 10.31083/j.jin.2019.03.164
    In the electroencephalogram recorded data are often confounded with artifacts, especially in the case of eye blinks. Different methods for artifact detection and removal are discussed in the literature, including automatic detection and removal. Here, an automatic method of eye blink detection and correction is proposed where sparse coding is used for an electroencephalogram dataset. In this method, a hybrid dictionary based on a ridgelet transformation is used to capture prominent features by analyzing independent components extracted from a different number of electroencephalogram channels. In this study, the proposed method has been tested and validated with five different datasets for artifact detection and correction. Results show that the proposed technique is promising as it successfully extracted the exact locations of eye blinking artifacts. The accuracy of the method (automatic detection) is 89.6% which represents a better estimate than that obtained by an extreme machine learning classifier.
    Matched MeSH terms: Electroencephalography
  14. Habib MA, Ibrahim F, Mohktar MS, Kamaruzzaman SB, Lim KS
    Clin Neurophysiol, 2020 03;131(3):642-654.
    PMID: 31978849 DOI: 10.1016/j.clinph.2019.11.058
    OBJECTIVE: This study aimed to present a new ictal component selection technique, named as recursive ICA-decomposition for ictal component selection (RIDICS), for potential application in epileptogenic zone localization.

    METHODS: The proposed technique decomposes ictal EEG recursively, eliminates a few unwanted components in every recursive cycle, and finally selects the most significant ictal component. Back-projected EEG, regenerated from that component, was used for source estimation. Fifty sets of simulated EEGs and 24 seizures in 8 patients were analyzed. Dipole sources of simulated-EEGs were compared with a known dipole location whereas epileptogenic zones of the seizures were compared with their corresponding sites of successful surgery. The RIDICS technique was compared with a conventional technique.

    RESULTS: The RIDICS technique estimated the dipole sources at an average distance of 12.86 mm from the original dipole location, shorter than the distances obtained using the conventional technique. Epileptogenic zones of the patients, determined by the RIDICS technique, were highly concordant with the sites of surgery with a concordance rate of 83.33%.

    CONCLUSIONS: Results show that the RIDICS technique can be a promising quantitative technique for ictal component selection.

    SIGNIFICANCE: Properly selected ictal component gives good approximation of epileptogenic zone, which eventually leads to successful epilepsy surgery.

    Matched MeSH terms: Electroencephalography
  15. Ong JS, Wong SN, Arulsamy A, Watterson JL, Shaikh MF
    Curr Neuropharmacol, 2022;20(5):950-964.
    PMID: 34749622 DOI: 10.2174/1570159X19666211108153001
    BACKGROUND: Epilepsy is a devastating neurological disorder that affects nearly 70 million people worldwide. Epilepsy causes uncontrollable, unprovoked and unpredictable seizures that reduce the quality of life of those afflicted, with 1-9 epileptic patient deaths per 1000 patients occurring annually due to sudden unexpected death in epilepsy (SUDEP). Predicting the onset of seizures and managing them may help patients from harming themselves and may improve their well-being. For a long time, electroencephalography (EEG) devices have been the mainstay for seizure detection and monitoring. This systematic review aimed to elucidate and critically evaluate the latest advancements in medical devices, besides EEG, that have been proposed for the management and prediction of epileptic seizures. A literature search was performed on three databases, PubMed, Scopus and EMBASE.

    METHODS: Following title/abstract screening by two independent reviewers, 27 articles were selected for critical analysis in this review.

    RESULTS: These articles revealed ambulatory, non-invasive and wearable medical devices, such as the in-ear EEG devices; the accelerometer-based devices and the subcutaneous implanted EEG devices might be more acceptable than traditional EEG systems. In addition, extracerebral signalbased devices may be more efficient than EEG-based systems, especially when combined with an intervention trigger. Although further studies may still be required to improve and validate these proposed systems before commercialization, these findings may give hope to epileptic patients, particularly those with refractory epilepsy, to predict and manage their seizures.

    CONCLUSION: The use of medical devices for epilepsy may improve patients' independence and quality of life and possibly prevent sudden unexpected death in epilepsy (SUDEP).

    Matched MeSH terms: Electroencephalography
  16. 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*
  17. Alyan E, Saad NM, Kamel N, Yusoff MZ, Zakariya MA, Rahman MA, et al.
    Sensors (Basel), 2021 Mar 11;21(6).
    PMID: 33799722 DOI: 10.3390/s21061968
    This study aims to investigate the effects of workplace noise on neural activity and alpha asymmetries of the prefrontal cortex (PFC) during mental stress conditions. Workplace noise exposure is a pervasive environmental pollutant and is negatively linked to cognitive effects and selective attention. Generally, the stress theory is assumed to underlie the impact of noise on health. Evidence for the impacts of workplace noise on mental stress is lacking. Fifteen healthy volunteer subjects performed the Montreal imaging stress task in quiet and noisy workplaces while their brain activity was recorded using electroencephalography. The salivary alpha-amylase (sAA) was measured before and immediately after each tested workplace to evaluate the stress level. The results showed a decrease in alpha rhythms, or an increase in cortical activity, of the PFC for all participants at the noisy workplace. Further analysis of alpha asymmetry revealed a greater significant relative right frontal activation of the noisy workplace group at electrode pairs F4-F3 but not F8-F7. Furthermore, a significant increase in sAA activity was observed in all participants at the noisy workplace, demonstrating the presence of stress. The findings provide critical information on the effects of workplace noise-related stress that might be neglected during mental stress evaluations.
    Matched MeSH terms: Electroencephalography*
  18. Awais MA, Yusoff MZ, Khan DM, Yahya N, Kamel N, Ebrahim M
    Sensors (Basel), 2021 Sep 30;21(19).
    PMID: 34640888 DOI: 10.3390/s21196570
    Motor imagery (MI)-based brain-computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain's neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms-an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain-computer interfaces.
    Matched MeSH terms: Electroencephalography*
  19. 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
  20. 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
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