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  1. Fulltext Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework
    Yahya N, Musa H, Ong ZY, Elamvazuthi I
    Sensors (Basel), 2019 Nov 08;19(22).
    PMID: 31717412 DOI: 10.3390/s19224878
    In this work, an algorithm for the classification of six motor functions from an electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and a continuous wavelet transform (CWT), is investigated. The EEG data comprise six grasp-and-lift events, which are used to investigate the potential of using EEG as input signals with brain computer interface devices for controlling prosthetic devices for upper limb movement. Selected EEG channels are the ones located over the motor cortex, C3, Cz and C4, as well as at the parietal region, P3, Pz and P4. In general, the proposed algorithm includes three main stages, band pass filtering, CSP filtering, and wavelet transform and training on GoogLeNet for feature extraction, feature learning and classification. The band pass filtering is performed to select the EEG signal in the band of 7 Hz to 30 Hz while eliminating artifacts related to eye blink, heartbeat and muscle movement. The CSP filtering is applied on two-class EEG signals that will result in maximizing the power difference between the two-class dataset. Since CSP is mathematically developed for two-class events, the extension to the multiclass paradigm is achieved by using the approach of one class versus all other classes. Subsequently, continuous wavelet transform is used to convert the band pass and CSP filtered signals from selected electrodes to scalograms which are then converted to images in grayscale format. The three scalograms from the motor cortex regions and the parietal region are then combined to form two sets of RGB images. Next, these RGB images become the input to GoogLeNet for classification of the motor EEG signals. The performance of the proposed classification algorithm is evaluated in terms of precision, sensitivity, specificity, accuracy with average values of 94.8%, 93.5%, 94.7%, 94.1%, respectively, and average area under the receiver operating characteristic (ROC) curve equal to 0.985. These results indicate a good performance of the proposed algorithm in classifying grasp-and-lift events from EEG signals.
    Matched MeSH terms: Electroencephalography/methods*
  2. Fulltext Reliable epileptic seizure detection using an improved wavelet neural network
    Zainuddin Z, Huong LK, Pauline O
    Australas Med J, 2013;6(5):308-14.
    PMID: 23745153 DOI: 10.4066/AMJ.2013.1640
    Electroencephalogram (EEG) signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts.
    Matched MeSH terms: Electroencephalography
  3. Fulltext Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future
    Huggins JE, Guger C, Ziat M, Zander TO, Taylor D, Tangermann M, et al.
    Brain Comput Interfaces (Abingdon), 2017;4(1-2):3-36.
    PMID: 29152523 DOI: 10.1080/2326263X.2016.1275488
    The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.
    Matched MeSH terms: Electroencephalography
  4. Fulltext An Emotion Assessment of Stroke Patients by Using Bispectrum Features of EEG Signals
    Yean CW, Wan Ahmad WK, Mustafa WA, Murugappan M, Rajamanickam Y, Adom AH, et al.
    Brain Sci, 2020 Sep 25;10(10).
    PMID: 32992930 DOI: 10.3390/brainsci10100672
    Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states. The estimated bispectrum mapped in the contour plots show the different appearance of nonlinearity in the EEG signals for different emotional states. Bispectrum features were extracted from the alpha (8-13) Hz, beta (13-30) Hz and gamma (30-49) Hz bands, respectively. The k-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers were used to classify the six emotions in LBD, RBD and NC. The bispectrum features showed statistical significance for all three groups. The beta frequency band was the best performing EEG frequency-sub band for emotion classification. The combination of alpha to gamma bands provides the highest classification accuracy in both KNN and PNN classifiers. Sadness emotion records the highest classification, which was 65.37% in LBD, 71.48% in RBD and 75.56% in NC groups.
    Matched MeSH terms: Electroencephalography
  5. Decoding of the relationship between human brain activity and walking paths
    Kamal SM, Sim S, Tee R, Nathan V, Aghasian E, Namazi H
    Technol Health Care, 2020;28(4):381-390.
    PMID: 31796717 DOI: 10.3233/THC-191965
    BACKGROUND: The human brain controls all actions of the body. Walking is one of the most important actions that deals with the movement of the body. In fact, the brain controls and regulates human walking based on different conditions. One of the conditions that affects human walking is the complexity of path of movement. Therefore, the brain activity should change when a person walks on a path with different complexities.

    OBJECTIVE: In this research we benefit from fractal analysis to study the effect of complexity of path of movement on the complexity of human brain reaction.

    METHODS: For this purpose we calculate the fractal dimension of the electroencephalography (EEG) signal when subjects walk on different paths with different fractal dimensions (complexity).

    RESULTS: The results of the analysis show that the complexity of brain activity increases with the increment of complexity of path of movement.

    CONCLUSION: The method of analysis employed in this research can also be employed to analyse the reaction of the human heart and respiration when subjects move on paths with different complexities.

    Matched MeSH terms: Electroencephalography
  6. 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/classification; Electroencephalography/methods*; Electroencephalography/statistics & numerical data
  7. Fulltext Machine Learning-Based Epileptic Seizure Detection Methods Using Wavelet and EMD-Based Decomposition Techniques: A Review
    Thangarajoo RG, Reaz MBI, Srivastava G, Haque F, Ali SHM, Bakar AAA, et al.
    Sensors (Basel), 2021 Dec 20;21(24).
    PMID: 34960577 DOI: 10.3390/s21248485
    Epileptic seizures are temporary episodes of convulsions, where approximately 70 percent of the diagnosed population can successfully manage their condition with proper medication and lead a normal life. Over 50 million people worldwide are affected by some form of epileptic seizures, and their accurate detection can help millions in the proper management of this condition. Increasing research in machine learning has made a great impact on biomedical signal processing and especially in electroencephalogram (EEG) data analysis. The availability of various feature extraction techniques and classification methods makes it difficult to choose the most suitable combination for resource-efficient and correct detection. This paper intends to review the relevant studies of wavelet and empirical mode decomposition-based feature extraction techniques used for seizure detection in epileptic EEG data. The articles were chosen for review based on their Journal Citation Report, feature selection methods, and classifiers used. The high-dimensional EEG data falls under the category of '3N' biosignals-nonstationary, nonlinear, and noisy; hence, two popular classifiers, namely random forest and support vector machine, were taken for review, as they are capable of handling high-dimensional data and have a low risk of over-fitting. The main metrics used are sensitivity, specificity, and accuracy; hence, some papers reviewed were excluded due to insufficient metrics. To evaluate the overall performances of the reviewed papers, a simple mean value of all metrics was used. This review indicates that the system that used a Stockwell transform wavelet variant as a feature extractor and SVM classifiers led to a potentially better result.
    Matched MeSH terms: Electroencephalography
  8. Statistical models for brain signals with properties that evolve across trials
    Ombao H, Fiecas M, Ting CM, Low YF
    Neuroimage, 2018 Oct 15;180(Pt B):609-618.
    PMID: 29223740 DOI: 10.1016/j.neuroimage.2017.11.061
    Most neuroscience cognitive experiments involve repeated presentations of various stimuli across several minutes or a few hours. It has been observed that brain responses, even to the same stimulus, evolve over the course of the experiment. These changes in brain activation and connectivity are believed to be associated with learning and/or habituation. In this paper, we present two general approaches to modeling dynamic brain connectivity using electroencephalograms (EEGs) recorded across replicated trials in an experiment. The first approach is the Markovian regime-switching vector autoregressive model (MS-VAR) which treats EEGs as realizations of an underlying brain process that switches between different states both within a trial and across trials in the entire experiment. The second is the slowly evolutionary locally stationary process (SEv-LSP) which characterizes the observed EEGs as a mixture of oscillatory activities at various frequency bands. The SEv-LSP model captures the dynamic nature of the amplitudes of the band-oscillations and cross-correlations between them. The MS-VAR model is able to capture abrupt changes in the dynamics while the SEv-LSP directly gives interpretable results. Moreover, it is nonparametric and hence does not suffer from model misspecification. For both of these models, time-evolving connectivity metrics in the frequency domain are derived from the model parameters for both functional and effective connectivity. We illustrate these two models for estimating cross-trial connectivity in selective attention using EEG data from an oddball paradigm auditory experiment where the goal is to characterize the evolution of brain responses to target stimuli and to standard tones presented randomly throughout the entire experiment. The results suggest dynamic changes in connectivity patterns over trials with inter-subject variability.
    Matched MeSH terms: Electroencephalography
  9. Lithium neurotoxicity with electroencephalogram changes
    Tan HJ, Lim KY, Rajah R, Ng CF
    BMJ Case Rep, 2021 Nov 17;14(11).
    PMID: 34789530 DOI: 10.1136/bcr-2021-246499
    Lithium is a medication with a variety of medical usage for various diseases including bipolar mood disorder. As the therapeutic window of lithium is narrow, its usage is commonly associated with toxicity. Lithium toxicity affects multiple systems especially the central nervous system, leading to neuropsychiatric complications. Haemodialysis is an effective method for lithium removal especially in severe lithium toxicity such as neurotoxicity with electroencephalogram changes. We describe a case of lithium neurotoxicity with electroencephalographic abnormalities which was reversed following haemodialysis.
    Matched MeSH terms: Electroencephalography
  10. The applications of the pilocarpine animal model of status epilepticus: 40 years of progress (1983-2023)
    Che Has AT
    Behav Brain Res, 2023 Aug 24;452:114551.
    PMID: 37348654 DOI: 10.1016/j.bbr.2023.114551
    Status epilepticus is a neurological disorder that can result in various neuropathological conditions and presentations. Various studies involving animal models have been accomplished to understand and replicating its prominent manifestations including characteristics of related clinical cases. Up to these days, there are variety of methods and techniques to be utilized in inducing this disorder that can be chemically or electrically applied which depending on the experimental designs and targets of the studies. In particular, the chemically induced pilocarpine animal model of status epilepticus is a reliable choice which has evolved for 40 years from its initial discovery back in 1983. Although the development of the model can be considered as a remarkable breakthrough in understanding status epilepticus, several aspects of the model have been improved, throughout the years. Among the major issues in developing this model are the morbidity and mortality rates during induction process. Several modifications have been introduced in the process by different studies to tackle the related problems including application of dose fractionation, adaptation of pilocarpine to lithium-pilocarpine model and utilization of various drugs. Despite all challenges and drawbacks, this model has proven its pertinent and relevance with improvements that have been adapted since it was introduced 40 years ago. In this review, we emphasize on the evolution of this animal model from the beginning until now (1983 - 2023) and the related issues that have made this model still a popular choice in status epilepticus studies.
    Matched MeSH terms: Electroencephalography
  11. Functional magnetic resonance imaging (FMRI) neurofeedback: implementations and applications
    Dewiputri WI, Auer T
    Malays J Med Sci, 2013 Oct;20(5):5-15.
    PMID: 24643368
    Neurofeedback (NFB) allows subjects to learn how to volitionally influence the neuronal activation in the brain by employing real-time neural activity as feedback. NFB has already been performed with electroencephalography (EEG) since the 1970s. Functional MRI (fMRI), offering a higher spatial resolution, has further increased the spatial specificity. In this paper, we briefly outline the general principles behind NFB, the implementation of fMRI-NFB studies, the feasibility of fMRI-NFB, and the application of NFB as a supplementary therapy tool.
    Matched MeSH terms: Electroencephalography
  12. Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques
    Amin HU, Malik AS, Ahmad RF, Badruddin N, Kamel N, Hussain M, et al.
    Australas Phys Eng Sci Med, 2015 Mar;38(1):139-49.
    PMID: 25649845 DOI: 10.1007/s13246-015-0333-x
    This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task--Raven's advance progressive metric test and (2) the EEG signals recorded in rest condition--eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53-3.06 and 3.06-6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.
    Matched MeSH terms: Electroencephalography/classification*
  13. EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms
    Ahirwal MK, Kumar A, Singh GK
    IEEE/ACM Trans Comput Biol Bioinform, 2013 Nov-Dec;10(6):1491-504.
    PMID: 24407307 DOI: 10.1109/TCBB.2013.119
    This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.
    Matched MeSH terms: Electroencephalography/methods*
  14. Human memory retention and recall processes. A review of EEG and fMRI studies
    Amin H, Malik AS
    Neurosciences (Riyadh), 2013 Oct;18(4):330-44.
    PMID: 24141456
    Human memory is an important concept in cognitive psychology and neuroscience. Our brain is actively engaged in functions of learning and memorization. Generally, human memory has been classified into 2 groups: short-term/working memory, and long-term memory. Using different memory paradigms and brain mapping techniques, psychologists and neuroscientists have identified 3 memory processes: encoding, retention, and recall. These processes have been studied using EEG and functional MRI (fMRI) in cognitive and neuroscience research. This study reviews previous research reported for human memory processes, particularly brain behavior in memory retention and recall processes with the use of EEG and fMRI. We discuss issues and challenges related to memory research with EEG and fMRI techniques.
    Matched MeSH terms: Electroencephalography*
  15. Single-trial subspace-based approach for VEP extraction
    Kamel N, Yusoff MZ, Hani AF
    IEEE Trans Biomed Eng, 2011 May;58(5):1383-93.
    PMID: 21177154 DOI: 10.1109/TBME.2010.2101073
    A signal subspace approach for extracting visual evoked potentials (VEPs) from the background electroencephalogram (EEG) colored noise without the need for a prewhitening stage is proposed. Linear estimation of the clean signal is performed by minimizing signal distortion while maintaining the residual noise energy below some given threshold. The generalized eigendecomposition of the covariance matrices of a VEP signal and brain background EEG noise is used to transform them jointly to diagonal matrices. The generalized subspace is then decomposed into signal subspace and noise subspace. Enhancement is performed by nulling the components in the noise subspace and retaining the components in the signal subspace. The performance of the proposed algorithm is tested with simulated and real data, and compared with the recently proposed signal subspace techniques. With the simulated data, the algorithms are used to estimate the latencies of P(100), P(200), and P(300) of VEP signals corrupted by additive colored noise at different values of SNR. With the real data, the VEP signals are collected at Selayang Hospital, Kuala Lumpur, Malaysia, and the capability of the proposed algorithm in detecting the latency of P(100) is obtained and compared with other subspace techniques. The ensemble averaging technique is used as a baseline for this comparison. The results indicated significant improvement by the proposed technique in terms of better accuracy and less failure rate.
    Matched MeSH terms: Electroencephalography/methods*
  16. Context based error modeling for lossless compression of EEG signals using neural networks
    Sriraam N, Eswaran C
    J Med Syst, 2006 Dec;30(6):439-48.
    PMID: 17233156
    Two-stage lossless data compression methods involving predictors and encoders are well known. This paper discusses the application of context based error modeling techniques for neural network predictors used for the compression of EEG signals. Error modeling improves the performance of a compression algorithm by removing the statistical redundancy that exists among the error signals after the prediction stage. In this paper experiments are carried out by using human EEG signals recorded under various physiological conditions to evaluate the effect of context based error modeling in the EEG compression. It is found that the compression efficiency of the neural network based predictive techniques is significantly improved by using the error modeling schemes. It is shown that the bits per sample required for EEG compression with error modeling and entropy coding lie in the range of 2.92 to 6.62 which indicates a saving of 0.3 to 0.7 bits compared to the compression scheme without error modeling.
    Matched MeSH terms: Electroencephalography/methods*
  17. Artificial neural network based epileptic detection using time-domain and frequency-domain features
    Srinivasan V, Eswaran C, Sriraam N
    J Med Syst, 2005 Dec;29(6):647-60.
    PMID: 16235818
    Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The long-term EEG recordings of an epileptic patient obtained from the ambulatory recording systems contain a large volume of EEG data. Detection of the epileptic activity requires a time consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper discusses an automated diagnostic method for epileptic detection using a special type of recurrent neural network known as Elman network. The experiments are carried out by using time-domain as well as frequency-domain features of the EEG signal. Experimental results show that Elman network yields epileptic detection accuracy rates as high as 99.6% with a single input feature which is better than the results obtained by using other types of neural networks with two and more input features.
    Matched MeSH terms: Electroencephalography/methods*
  18. 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*
  19. Infantile spasms in a Malaysian population. Clinical and EEG studies at initial presentation
    Vignaendra V, Raju L, Lee C, Ghee T
    J Trop Pediatr Environ Child Health, 1976 Oct;22(5):220-4.
    PMID: 1051827
    Matched MeSH terms: Electroencephalography*
  20. Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals
    Michielli N, Acharya UR, Molinari F
    Comput Biol Med, 2019 03;106:71-81.
    PMID: 30685634 DOI: 10.1016/j.compbiomed.2019.01.013
    Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in neurological and clinical studies. NCP represents the mental/cognitive human capacity in performing a specific task. It is difficult to develop the study protocols as the subject's NCP changes in a known predictable way. Sleep is time-varying NCP and can be used to develop novel NCP techniques. Accurate analysis and interpretation of human sleep electroencephalographic (EEG) signals is needed for proper NCP assessment. In addition, sleep deprivation may cause prominent cognitive risks in performing many common activities such as driving or controlling a generic device; therefore, sleep scoring is a crucial part of the process. In the sleep cycle, the first stage of non-rapid eye movement (NREM) sleep or stage N1 is the transition between wakefulness and drowsiness and becomes relevant for the study of NCP. In this study, a novel cascaded recurrent neural network (RNN) architecture based on long short-term memory (LSTM) blocks, is proposed for the automated scoring of sleep stages using EEG signals derived from a single-channel. Fifty-five time and frequency-domain features were extracted from the EEG signals and fed to feature reduction algorithms to select the most relevant ones. The selected features constituted as the inputs to the LSTM networks. The cascaded architecture is composed of two LSTM RNNs: the first network performed 4-class classification (i.e. the five sleep stages with the merging of stages N1 and REM into a single stage) with a classification rate of 90.8%, and the second one obtained a recognition performance of 83.6% for 2-class classification (i.e. N1 vs REM). The overall percentage of correct classification for five sleep stages is found to be 86.7%. The objective of this work is to improve classification performance in sleep stage N1, as a first step of NCP assessment, and at the same time obtain satisfactory classification results in the other sleep stages.
    Matched MeSH terms: Electroencephalography*
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